code stringlengths 281 23.7M |
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()
('-wmt')
('-lang')
('-sys_name')
('-src_ref', help='src or ref')
('-loaded', type=bool, default=False)
('-ckpt_addr', help='LLama_finetune_april_8/checkpoint-148', default=None)
('-start_index', type=int)
('-end_index', type=int)
('-batch_size', type=int)
('-sample', type=bool)
('-num_ret', type=int)
('-task_mode', type=str, help='evaluation or machine translation')
def main(wmt, lang, loaded, src_ref, sys_name, ckpt_addr, start_index, end_index, batch_size, sample, num_ret, task_mode):
if (not loaded):
if (lang == 'zh-en'):
lang_code = 'Chinese-to-English'
elif (lang == 'en-de'):
lang_code = 'English-to-German'
elif (lang == 'en-ru'):
lang_code = 'English-to-Russian'
else:
print('Language dir is not existed')
exit(1)
from mt_metrics_eval import data
if (not os.path.isdir(f'test_{wmt}_{lang}')):
os.makedirs(f'test_{wmt}_{lang}')
os.makedirs(f'test_{wmt}_{lang}/src')
os.makedirs(f'test_{wmt}_{lang}/ref')
index = ckpt_addr.split('-')[(- 1)]
print(index)
os.makedirs(f'test_{wmt}_{lang}/SEScore3_output_{index}')
evs = data.EvalSet(wmt, lang)
mqm_scores = evs.Scores('seg', 'mqm')
print('ref: ', evs.std_ref)
print('Annotated System: ', len(mqm_scores))
for (sys_name, score_ls) in mqm_scores.items():
assert (len(score_ls) == len(evs.sys_outputs[sys_name]))
if (sys_name != evs.std_ref):
final_ref_dict = {'type': 'text2score', 'instances': []}
final_src_dict = {'type': 'text2score', 'instances': []}
for (index, (score, output)) in enumerate(zip(score_ls, evs.sys_outputs[sys_name])):
if (sys_name != evs.std_ref):
if (score != None):
ref = evs.sys_outputs[evs.std_ref][index]
cand = evs.sys_outputs[sys_name][index]
src = evs.src[index]
ref_prompt = f"""You are evaluating {lang_code} Machine translation task. The correct translation is "{ref}". The model generated translation is "{cand}". Please identify all errors within each model output, up to a maximum of five. For each error, please give me the corresponding error type, major/minor label, error location of the model generated translation and explanation for the error. Major errors can confuse or mislead the reader due to significant change in meaning, while minor errors don't lead to loss of meaning but will be noticed."""
src_prompt = f"""You are evaluating {lang_code} Machine translation task. The source is "{src}". The model generated translation is "{cand}". Please identify all errors within each model output, up to a maximum of five. For each error, please give me the corresponding error type, major/minor label, error location of the model generated translation and explanation for the error. Major errors can confuse or mislead the reader due to significant change in meaning, while minor errors don't lead to loss of meaning but will be noticed."""
final_ref_dict['instances'] += [{'input': ref_prompt, 'output': score}]
final_src_dict['instances'] += [{'input': src_prompt, 'output': score}]
if (len(final_ref_dict['instances']) > 0):
with open(f'test_{wmt}_{lang}/ref/test_{wmt}_{lang}_{sys_name}_llama_ref_data.json', 'w') as f:
json.dump(final_ref_dict, f)
if (len(final_src_dict['instances']) > 0):
with open(f'test_{wmt}_{lang}/src/test_{wmt}_{lang}_{sys_name}_llama_src_data.json', 'w') as f:
json.dump(final_src_dict, f)
print(f'test_{wmt}_{lang}_{sys_name} ref and src files are saved!')
else:
if (task_mode == 'evaluation'):
KEY_TYPE = 'type'
KEY_INSTANCES = 'instances'
with open(f'test_{wmt}_{lang}/{src_ref}/test_{wmt}_{lang}_{sys_name}_llama_{src_ref}_data.json') as fin:
json_data = json.load(fin)
if (KEY_TYPE not in json_data.keys()):
raise ValueError(f'''"{KEY_TYPE}" field must be specified for data, e.g.{{
"{KEY_TYPE}: "text2text",
"{KEY_INSTANCES}": [
{{ "text": "Sentence 1: This is a sentence." }}
{{ "text": "Sentence 2: This is another sentence." }}
]
}}''')
device_id = (torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'))
model = LlamaForCausalLM.from_pretrained(ckpt_addr).to(device_id)
model.eval()
smart_tokenizer_and_embedding_resize(special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), tokenizer=tokenizer)
print('Vocab Size: ', len(tokenizer))
print('Loaded in model and tokenizer!')
index = ckpt_addr.split('-')[(- 1)]
if sample:
save_file = open(f'test_{wmt}_{lang}/SEScore3_output_sample_{index}/test_{wmt}_{lang}_{sys_name}_llama_{src_ref}_data_{start_index}_{end_index}_sample_{num_ret}.txt', 'w')
else:
save_file = open(f'test_{wmt}_{lang}/SEScore3_output_{index}/test_{wmt}_{lang}_{sys_name}_llama_{src_ref}_data_{start_index}_{end_index}.txt', 'w')
global_step = 0
with torch.no_grad():
with tqdm(total=(int((len(json_data['instances'][start_index:end_index]) / batch_size)) + 1)) as pbar:
for txts_dict in batchify(json_data['instances'][start_index:end_index], batch_size):
batch_txts = [txt_dict['input'] for txt_dict in txts_dict]
inputs = tokenizer(batch_txts, return_tensors='pt', padding=True, truncation=True, max_length=MAX_SOURCE_LENGTH)
try:
if sample:
outputs = model.generate(inputs['input_ids'].to(device_id), attention_mask=inputs['attention_mask'].to(device_id), max_new_tokens=MAX_TARGET_LENGTH, do_sample=True, top_p=0.95, temperature=0.8, num_return_sequences=num_ret)
else:
outputs = model.generate(inputs['input_ids'].to(device_id), attention_mask=inputs['attention_mask'].to(device_id), max_new_tokens=MAX_TARGET_LENGTH)
batch_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)
if sample:
for (index, output) in enumerate(batch_outputs):
save_file.write((((((str((global_step + start_index)) + '\t') + str(index)) + '\t') + output) + '[SEP_WENDA]'))
global_step += 1
else:
for output in batch_outputs:
save_file.write((((str((global_step + start_index)) + '\t') + output) + '[SEP_WENDA]'))
global_step += 1
except Exception as e:
print(f'ERROR: {e}')
pbar.update(1)
print('File is saved!') |
def brush_stroke_mask(img, color=(255, 255, 255)):
min_num_vertex = 8
max_num_vertex = 28
mean_angle = ((2 * math.pi) / 5)
angle_range = ((2 * math.pi) / 15)
min_width = 12
max_width = 80
def generate_mask(H, W, img=None):
average_radius = (math.sqrt(((H * H) + (W * W))) / 8)
mask = Image.new('RGB', (W, H), 0)
if (img is not None):
mask = img
for _ in range(np.random.randint(1, 4)):
num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
angle_min = (mean_angle - np.random.uniform(0, angle_range))
angle_max = (mean_angle + np.random.uniform(0, angle_range))
angles = []
vertex = []
for i in range(num_vertex):
if ((i % 2) == 0):
angles.append(((2 * math.pi) - np.random.uniform(angle_min, angle_max)))
else:
angles.append(np.random.uniform(angle_min, angle_max))
(h, w) = mask.size
vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
for i in range(num_vertex):
r = np.clip(np.random.normal(loc=average_radius, scale=(average_radius // 2)), 0, (2 * average_radius))
new_x = np.clip((vertex[(- 1)][0] + (r * math.cos(angles[i]))), 0, w)
new_y = np.clip((vertex[(- 1)][1] + (r * math.sin(angles[i]))), 0, h)
vertex.append((int(new_x), int(new_y)))
draw = ImageDraw.Draw(mask)
width = int(np.random.uniform(min_width, max_width))
draw.line(vertex, fill=color, width=width)
for v in vertex:
draw.ellipse(((v[0] - (width // 2)), (v[1] - (width // 2)), (v[0] + (width // 2)), (v[1] + (width // 2))), fill=color)
return mask
(width, height) = img.size
mask = generate_mask(height, width, img)
return mask |
class DeformConv2dPackMore(DeformConv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, deformable_groups=1, im2col_step=64, bias=True, lr_mult=0.1):
super(DeformConv2dPackMore, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, deformable_groups, im2col_step, bias)
out_channels = (((self.deformable_groups * 2) * self.kernel_size[0]) * self.kernel_size[1])
self.conv_offset = nn.Sequential(nn.Conv2d(self.in_channels, (self.in_channels // 4), kernel_size=1, bias=False), nn.BatchNorm2d((self.in_channels // 4)), nn.ReLU(inplace=True), nn.Conv2d((self.in_channels // 4), out_channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, bias=True))
self.conv_offset[(- 1)].lr_mult = lr_mult
self.conv_offset[(- 1)].inited = True
self.init_offset()
def init_offset(self):
self.conv_offset[(- 1)].weight.data.zero_()
self.conv_offset[(- 1)].bias.data.zero_()
def forward(self, input):
offset = self.conv_offset(input)
bs = input.size()[0]
im2col_step = (bs // 2)
return DeformConv2dFunction.apply(input, offset, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups, im2col_step) |
def main(_):
if (not FLAGS.output_file):
raise ValueError('You must supply the path to save to with --output_file')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default() as graph:
dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train', FLAGS.dataset_dir)
network_fn = nets_factory.get_network_fn(FLAGS.model_name, num_classes=(dataset.num_classes - FLAGS.labels_offset), is_training=FLAGS.is_training)
image_size = (FLAGS.image_size or network_fn.default_image_size)
placeholder = tf.placeholder(name='input', dtype=tf.float32, shape=[FLAGS.batch_size, image_size, image_size, 3])
network_fn(placeholder)
graph_def = graph.as_graph_def()
with gfile.GFile(FLAGS.output_file, 'wb') as f:
f.write(graph_def.SerializeToString()) |
def test_repair_destroy_path():
(x, y, z) = inputs()
e1 = transpose_view(transpose_view(x))
e2 = transpose_view(transpose_view(e1))
e3 = add_in_place(e2, y)
e4 = add_in_place(e1, z)
g = create_fgraph([x, y, z], [e3, e4], False)
assert (not g.consistent())
g.replace(e2, transpose_view(x))
assert (not g.consistent()) |
def apply_to_tensor(f, sample):
if (len(sample) == 0):
return {}
def _apply(x):
if torch.is_tensor(x):
return f(x)
elif isinstance(x, dict):
return {key: _apply(value) for (key, value) in x.items()}
elif isinstance(x, list):
return [_apply(x) for x in x]
elif isinstance(x, tuple):
return [_apply(x) for x in x]
else:
return x
return _apply(sample) |
.end_to_end()
def test_if_skipif_decorator_is_applied_skipping(tmp_path):
source = '\n import pytask\n\n .skipif(condition=True, reason="bla")\n .produces("out.txt")\n def task_first():\n assert False\n\n .depends_on("out.txt")\n def task_second():\n assert False\n '
tmp_path.joinpath('task_module.py').write_text(textwrap.dedent(source))
session = build(paths=tmp_path)
node = session.collection_reports[0].node
assert (len(node.markers) == 1)
assert (node.markers[0].name == 'skipif')
assert (node.markers[0].args == ())
assert (node.markers[0].kwargs == {'condition': True, 'reason': 'bla'})
assert (session.execution_reports[0].outcome == TaskOutcome.SKIP)
assert isinstance(session.execution_reports[0].exc_info[1], Skipped)
assert (session.execution_reports[1].outcome == TaskOutcome.SKIP)
assert isinstance(session.execution_reports[1].exc_info[1], Skipped)
assert (session.execution_reports[0].exc_info[1].args[0] == 'bla') |
class BooleanTest(object):
def test_false(self):
assert (inputs.boolean('False') is False)
def test_0(self):
assert (inputs.boolean('0') is False)
def test_true(self):
assert (inputs.boolean('true') is True)
def test_1(self):
assert (inputs.boolean('1') is True)
def test_case(self):
assert (inputs.boolean('FaLSE') is False)
assert (inputs.boolean('FaLSE') is False)
def test_python_bool(self):
assert (inputs.boolean(True) is True)
assert (inputs.boolean(False) is False)
def test_bad_boolean(self):
with pytest.raises(ValueError):
inputs.boolean('blah')
with pytest.raises(ValueError):
inputs.boolean(None)
def test_checkbox(self):
assert (inputs.boolean('on') is True)
def test_non_strings(self):
assert (inputs.boolean(0) is False)
assert (inputs.boolean(1) is True)
assert (inputs.boolean([]) is False)
def test_schema(self):
assert (inputs.boolean.__schema__ == {'type': 'boolean'}) |
class RollingVirtualStorage(Loadable, Drawable, _core.RollingVirtualStorage, metaclass=NodeMeta):
__parameter_attributes__ = ('min_volume', 'max_volume')
__node_attributes__ = ('nodes',)
def __init__(self, model, name, nodes, **kwargs):
min_volume = pop_kwarg_parameter(kwargs, 'min_volume', 0.0)
if (min_volume is None):
min_volume = 0.0
max_volume = pop_kwarg_parameter(kwargs, 'max_volume', 0.0)
initial_volume = kwargs.pop('initial_volume', 0.0)
initial_volume_pc = kwargs.pop('initial_volume_pc', None)
cost = pop_kwarg_parameter(kwargs, 'cost', 0.0)
factors = kwargs.pop('factors', None)
days = kwargs.pop('days', None)
timesteps = kwargs.pop('timesteps', 0)
if ((not timesteps) and (not days)):
raise ValueError('Either `timesteps` or `days` must be specified.')
super().__init__(model, name, **kwargs)
self.min_volume = min_volume
self.max_volume = max_volume
self.initial_volume = initial_volume
self.initial_volume_pc = initial_volume_pc
self.cost = cost
self.nodes = nodes
self.days = days
self.timesteps = timesteps
if (factors is None):
self.factors = [1.0 for i in range(len(nodes))]
else:
self.factors = factors
def check(self):
super().check()
if (self.cost not in (0.0, None)):
raise NotImplementedError('RollingVirtualStorage does not currently support a non-zero cost.')
def setup(self, model):
if ((self.days is not None) and (self.days > 0)):
try:
self.timesteps = (self.days // self.model.timestepper.delta)
except TypeError:
raise TypeError('A rolling period defined as a number of days is only valid with daily time-steps.')
if (self.timesteps < 1):
raise ValueError('The number of time-steps for a RollingVirtualStorage node must be greater than one.')
super().setup(model) |
def adjust_assets(scenes):
sim = None
global_mapping_path = 'cos_eor/utils/global_mapping_v3_local.yaml'
if (os.path.exists(global_mapping_path) and False):
global_mapping = yaml.load(open(global_mapping_path, 'r'), Loader=yaml.BaseLoader)
else:
global_mapping = {'mapping_igib': {}, 'scenes_parsed': [], 'version_notes': 'Excluding pictures, floor lamps. Also, removed floating parts from articulate objects', 'receptacles': [], 'objects': []}
non_art_data_path = 'cos_eor/utils/non_art_scale_rotation_v2.yaml'
if os.path.exists(non_art_data_path):
with open(non_art_data_path, 'r') as f:
non_art_data = yaml.load(f, Loader=yaml.BaseLoader)['accepted']
for template in non_art_data:
global_mapping['objects'].append(non_art_data[template]['global_object'])
for (scene_id, scene) in tqdm(enumerate(scenes), desc='Adjusting iGibson', total=len(scenes)):
print(f'Scene: {scene}')
scene_key = os.path.split(scene)[(- 1)].split('.')[0]
if (scene_key in global_mapping['scenes_parsed']):
print(f'Skipping: {scene_key}')
continue
sim = get_sim(scene, sim)
(meta_keys, object_ids, metadata, metadata_dir) = sim.init_metadata_objects(metadata_file='metadata_v2.yaml')
if ('urdfs' in metadata):
metadata = metadata['urdfs']
debug_sim_viewer(sim, False, meta_keys, object_ids, metadata, metadata_dir, global_mapping, adjust_igib=True)
scale_rot_data = yaml.load(open('cos_eor/utils/art_scale_rotation_v3_sky.yaml'))
global_mapping['object_room_map'] = {}
for obj in scale_rot_data['accepted'].keys():
try:
rooms = set(scale_rot_data['accepted'][obj]['rooms'])
rooms = ['_'.join(r.split('_')[:(- 1)]) for r in rooms if (r is not None)]
except:
print('not found', obj)
continue
global_mapping['object_room_map'][obj] = rooms
with open(global_mapping_path, 'w') as f:
yaml.dump(global_mapping, f)
print(f'Dumped: {global_mapping_path}') |
def get_price(plan, require_business_plan):
if (not features.BILLING):
return
plan_found = None
for plan_obj in PLANS:
if (plan_obj['stripeId'] == plan):
plan_found = plan_obj
if ((not plan_found) or plan_found['deprecated']):
logger.warning('Plan not found or deprecated: %s', plan)
raise NotFound()
if (require_business_plan and (not plan_found['bus_features']) and (not (plan_found['price'] == 0))):
logger.warning('Business attempting to subscribe to personal plan: %s', plan_found['title'])
raise request_error(message='No matching plan found')
return plan_found |
def test_transform_types_not_params_array():
data = {'attr': [1, 2, 3]}
custom_types = {'attr': types.ArrayAttribute}
(new_data, files) = utils._transform_types(data, custom_types, transform_data=False)
assert (new_data is not data)
assert (new_data == data)
assert (files == {}) |
def create_report(seeds_dir: str, output_file: Path):
def item_creator():
return collections.defaultdict(list)
item_name_to_location = collections.defaultdict(item_creator)
seed_files = list(Path(seeds_dir).glob(f'**/*.{LayoutDescription.file_extension()}'))
seed_files.extend(Path(seeds_dir).glob('**/*.json'))
for seed in iterate_with_log(seed_files):
seed = typing.cast(Path, seed)
try:
seed_data = read_json(seed)
except json.JSONDecodeError:
continue
for item_order in seed_data['item_order']:
if (' as ' in item_order):
continue
(item_name, item_location) = item_order.split(' at ', 1)
item_name = _filter_item_name(item_name)
item_location = item_location.split(' with hint ', 1)[0]
if (('Expansion' in item_name) or (item_name == 'Energy Tank')):
continue
item_name_to_location[item_name][item_location].append(str(seed.relative_to(seeds_dir)))
final_results = {item_name: {location: seeds for (location, seeds) in sorted(locations.items(), key=(lambda it: len(it[1]))) if (len(seeds) < 250)} for (item_name, locations) in sorted(item_name_to_location.items(), key=(lambda it: it[0]))}
json_lib.write_path(output_file, final_results) |
def get_release_notes_template(template_dir: Path) -> str:
fname = (template_dir / '.release_notes.md.j2')
try:
return fname.read_text(encoding='utf-8')
except FileNotFoundError:
return files('semantic_release').joinpath('data/templates/release_notes.md.j2').read_text(encoding='utf-8') |
class RateLimitError(APIError):
def __init__(self, *args, **kwargs):
self.response_headers = kwargs.pop('response_headers', None)
self.rl_limit = int(self.response_headers.get('X-Ratelimit-Limit'))
self.rl_reset = datetime.fromtimestamp(int(self.response_headers.get('X-Ratelimit-Reset')))
super().__init__('Too many requests. Limit: {} Reset: {}'.format(self.rl_limit, self.rl_reset)) |
def ksboolean(value):
try:
if (value.lower() in ('on', 'yes', 'true', '1')):
return True
elif (value.lower() in ('off', 'no', 'false', '0')):
return False
else:
raise ArgumentTypeError((_('invalid boolean value: %r') % value))
except AttributeError:
raise ArgumentTypeError((_('invalid boolean value: %r') % value)) |
class EthicsUtilitarianism(Ethics):
VERSION = 0
DATASET_NAME = 'utilitarianism'
def training_docs(self):
for doc in self.dataset['train']:
(yield self._process_doc(doc))
def validation_docs(self):
raise NotImplementedError
def test_docs(self):
for doc in self.dataset['test']:
(yield self._process_doc(doc))
def _process_doc(self, doc):
rnd = random.Random(doc['activity'])
scenarios = [doc['activity'], doc['baseline']]
ordering = [0, 1]
rnd.shuffle(ordering)
return {'scenarios': [scenarios[ordering[0]], scenarios[ordering[1]]], 'label': int((ordering.index(0) == 0))}
def doc_to_text(self, doc):
return 'Scenario 1: {}\nScenario 2: {}\nQuestion: Is Scenario 1 preferable?\nAnswer:'.format(doc['scenarios'][0], doc['scenarios'][1])
def doc_to_target(self, doc):
return (' ' + yesno(doc['label']))
def construct_requests(self, doc, ctx):
(ll_yes, _) = rf.loglikelihood(ctx, ' yes')
(ll_no, _) = rf.loglikelihood(ctx, ' no')
return (ll_yes, ll_no)
def process_results(self, doc, results):
(ll_yes, ll_no) = results
pred = (ll_yes > ll_no)
gold = doc['label']
return {'acc': (pred == gold)}
def aggregation(self):
return {'acc': mean}
def higher_is_better(self):
return {'acc': True} |
class ResAttNet(nn.Module):
def __init__(self, channels, init_block_channels, attentions, att_scales, in_channels=3, in_size=(224, 224), num_classes=1000):
super(ResAttNet, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.features = nn.Sequential()
self.features.add_module('init_block', ResAttInitBlock(in_channels=in_channels, out_channels=init_block_channels))
in_channels = init_block_channels
for (i, channels_per_stage) in enumerate(channels):
hourglass_depth = ((len(channels) - 1) - i)
stage = nn.Sequential()
for (j, out_channels) in enumerate(channels_per_stage):
stride = (1 if ((i == 0) or (j != 0)) else 2)
if attentions[i][j]:
stage.add_module('unit{}'.format((j + 1)), AttBlock(in_channels=in_channels, out_channels=out_channels, hourglass_depth=hourglass_depth, att_scales=att_scales))
else:
stage.add_module('unit{}'.format((j + 1)), ResBlock(in_channels=in_channels, out_channels=out_channels, stride=stride))
in_channels = out_channels
self.features.add_module('stage{}'.format((i + 1)), stage)
self.features.add_module('post_activ', PreActivation(in_channels=in_channels))
self.features.add_module('final_pool', nn.AvgPool2d(kernel_size=7, stride=1))
self.output = nn.Linear(in_features=in_channels, out_features=num_classes)
self._init_params()
def _init_params(self):
for (name, module) in self.named_modules():
if isinstance(module, nn.Conv2d):
init.kaiming_uniform_(module.weight)
if (module.bias is not None):
init.constant_(module.bias, 0)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), (- 1))
x = self.output(x)
return x |
(frozen=True)
class AM2RPerGameOptions(PerGameOptions):
input_path: (Path | None) = None
output_path: (Path | None) = None
def as_json(self) -> dict:
return {**super().as_json, 'input_path': (str(self.input_path) if (self.input_path is not None) else None), 'output_path': (str(self.output_path) if (self.output_path is not None) else None)}
def from_json(cls, value: dict) -> Self:
game = RandovaniaGame.AM2R
cosmetic_patches = game.data.layout.cosmetic_patches.from_json(value['cosmetic_patches'])
return cls(cosmetic_patches=cosmetic_patches, input_path=decode_if_not_none(value['input_path'], Path), output_path=decode_if_not_none(value['output_path'], Path)) |
_torch
_vision
class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = (GLPNImageProcessor if is_vision_available() else None)
def setUp(self):
self.image_processor_tester = GLPNImageProcessingTester(self)
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, 'do_resize'))
self.assertTrue(hasattr(image_processing, 'size_divisor'))
self.assertTrue(hasattr(image_processing, 'resample'))
self.assertTrue(hasattr(image_processing, 'do_rescale'))
def test_batch_feature(self):
pass
def test_call_pil(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
encoded_images = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertTrue(((encoded_images.shape[(- 1)] % self.image_processor_tester.size_divisor) == 0))
self.assertTrue(((encoded_images.shape[(- 2)] % self.image_processor_tester.size_divisor) == 0))
def test_call_numpy(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
encoded_images = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertTrue(((encoded_images.shape[(- 1)] % self.image_processor_tester.size_divisor) == 0))
self.assertTrue(((encoded_images.shape[(- 2)] % self.image_processor_tester.size_divisor) == 0))
def test_call_pytorch(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
encoded_images = image_processing(image_inputs[0], return_tensors='pt').pixel_values
self.assertTrue(((encoded_images.shape[(- 1)] % self.image_processor_tester.size_divisor) == 0))
self.assertTrue(((encoded_images.shape[(- 2)] % self.image_processor_tester.size_divisor) == 0)) |
def init(app_name):
global APP_NAME, DBUS_IFACE
APP_NAME = app_name
name = 'org.freedesktop.Notifications'
path = '/org/freedesktop/Notifications'
interface = 'org.freedesktop.Notifications'
mainloop = None
if (DBusGMainLoop is not None):
mainloop = DBusGMainLoop(set_as_default=True)
bus = dbus.SessionBus(mainloop)
proxy = bus.get_object(name, path)
DBUS_IFACE = dbus.Interface(proxy, interface)
if (mainloop is not None):
DBUS_IFACE.connect_to_signal('ActionInvoked', _onActionInvoked)
DBUS_IFACE.connect_to_signal('NotificationClosed', _onNotificationClosed) |
def test_list_type():
test_dict = {'type': 'list', 'values': {'type': 'int', 'bits': 32}}
recap_type = from_dict(test_dict)
assert isinstance(recap_type, ListType)
assert (recap_type.type_ == 'list')
assert isinstance(recap_type.values, IntType)
assert (recap_type.values.type_ == 'int')
assert (recap_type.values.bits == 32) |
class DcardPost(Base, Timestamp):
__tablename__ = 'dcard_posts'
id = sa.Column(sa.Integer, primary_key=True)
forum_id = sa.Column(sa.String(64), nullable=False)
forum_name = sa.Column(sa.String(64), nullable=False)
title = sa.Column(sa.String(64, collation='utf8mb4_unicode_ci'), nullable=False)
content = sa.Column(MEDIUMTEXT(charset='utf8mb4', collation='utf8mb4_unicode_ci'), nullable=False)
school = sa.Column(sa.String(64))
gender = sa.Column(sa.String(10))
topics = sa.Column(sa.JSON, default=[])
like_count = sa.Column(sa.Integer, nullable=False)
reactions = relationship('DcardReaction', back_populates='post', uselist=True, lazy='joined')
with_nickname = sa.Column(sa.Boolean, nullable=False)
anonymous_school = sa.Column(sa.Boolean, nullable=False)
anonymous_department = sa.Column(sa.Boolean, nullable=False)
media = sa.Column(sa.JSON, default=[])
comments = relationship('DcardComment', back_populates='post', uselist=True, lazy='joined') |
def test_parser(testcase: DataDrivenTestCase) -> None:
options = Options()
options.force_uppercase_builtins = True
options.hide_error_codes = True
if testcase.file.endswith('python310.test'):
options.python_version = (3, 10)
else:
options.python_version = defaults.PYTHON3_VERSION
source = '\n'.join(testcase.input)
comments = get_mypy_comments(source)
(changes, _) = parse_mypy_comments(comments, options)
options = options.apply_changes(changes)
try:
n = parse(bytes(source, 'ascii'), fnam='main', module='__main__', errors=Errors(options), options=options, raise_on_error=True)
a = n.str_with_options(options).split('\n')
except CompileError as e:
a = e.messages
assert_string_arrays_equal(testcase.output, a, f'Invalid parser output ({testcase.file}, line {testcase.line})') |
def boundary_err(y_pred, y_true, t, onsets_s, offsets_s, timebin_dur, n_timebin_from_onoffset, unlabeled_class=0):
frame_err_vec = (y_true != y_pred)
n_frame_err = int(frame_err_vec.sum().item())
unlabeled_err = np.logical_and(frame_err_vec, np.logical_or((y_true == unlabeled_class), (y_pred == unlabeled_class)))
t_unlabeled_err = t[unlabeled_err]
t_unlabeled_err_from_onset_offset = [min(np.abs((np.concatenate((onsets_s, offsets_s)) - a_time))) for a_time in t_unlabeled_err]
(counts, _) = np.histogram(t_unlabeled_err_from_onset_offset, bins=np.arange(0.0, 1.0, timebin_dur))
n_unlabeled_err_within_n_timebin = counts[:n_timebin_from_onoffset].sum()
return (n_unlabeled_err_within_n_timebin / n_frame_err) |
def _input():
print('Enter the visa centre: ')
visa_centre = input()
print('Enter the category: ')
category = input()
print('Enter the sub category: ')
sub_category = input()
logging.debug('Visa centre: {}, Category: {}, Sub-Category: {}'.format(visa_centre, category, sub_category))
return (visa_centre, category, sub_category) |
def initShaders():
global Shaders
Shaders = [ShaderProgram(None, []), ShaderProgram('balloon', [VertexShader('\n varying vec3 normal;\n void main() {\n // compute here for use in fragment shader\n normal = normalize(gl_NormalMatrix * gl_Normal);\n gl_FrontColor = gl_Color;\n gl_BackColor = gl_Color;\n gl_Position = ftransform();\n }\n '), FragmentShader('\n varying vec3 normal;\n void main() {\n vec4 color = gl_Color;\n color.w = min(color.w + 2.0 * color.w * pow(normal.x*normal.x + normal.y*normal.y, 5.0), 1.0);\n gl_FragColor = color;\n }\n ')]), ShaderProgram('viewNormalColor', [VertexShader('\n varying vec3 normal;\n void main() {\n // compute here for use in fragment shader\n normal = normalize(gl_NormalMatrix * gl_Normal);\n gl_FrontColor = gl_Color;\n gl_BackColor = gl_Color;\n gl_Position = ftransform();\n }\n '), FragmentShader('\n varying vec3 normal;\n void main() {\n vec4 color = gl_Color;\n color.x = (normal.x + 1.0) * 0.5;\n color.y = (normal.y + 1.0) * 0.5;\n color.z = (normal.z + 1.0) * 0.5;\n gl_FragColor = color;\n }\n ')]), ShaderProgram('normalColor', [VertexShader('\n varying vec3 normal;\n void main() {\n // compute here for use in fragment shader\n normal = normalize(gl_Normal);\n gl_FrontColor = gl_Color;\n gl_BackColor = gl_Color;\n gl_Position = ftransform();\n }\n '), FragmentShader('\n varying vec3 normal;\n void main() {\n vec4 color = gl_Color;\n color.x = (normal.x + 1.0) * 0.5;\n color.y = (normal.y + 1.0) * 0.5;\n color.z = (normal.z + 1.0) * 0.5;\n gl_FragColor = color;\n }\n ')]), ShaderProgram('shaded', [VertexShader('\n varying vec3 normal;\n void main() {\n // compute here for use in fragment shader\n normal = normalize(gl_NormalMatrix * gl_Normal);\n gl_FrontColor = gl_Color;\n gl_BackColor = gl_Color;\n gl_Position = ftransform();\n }\n '), FragmentShader('\n varying vec3 normal;\n void main() {\n float p = dot(normal, normalize(vec3(1.0, -1.0, -1.0)));\n p = p < 0. ? 0. : p * 0.8;\n vec4 color = gl_Color;\n color.x = color.x * (0.2 + p);\n color.y = color.y * (0.2 + p);\n color.z = color.z * (0.2 + p);\n gl_FragColor = color;\n }\n ')]), ShaderProgram('edgeHilight', [VertexShader('\n varying vec3 normal;\n void main() {\n // compute here for use in fragment shader\n normal = normalize(gl_NormalMatrix * gl_Normal);\n gl_FrontColor = gl_Color;\n gl_BackColor = gl_Color;\n gl_Position = ftransform();\n }\n '), FragmentShader('\n varying vec3 normal;\n void main() {\n vec4 color = gl_Color;\n float s = pow(normal.x*normal.x + normal.y*normal.y, 2.0);\n color.x = color.x + s * (1.0-color.x);\n color.y = color.y + s * (1.0-color.y);\n color.z = color.z + s * (1.0-color.z);\n gl_FragColor = color;\n }\n ')]), ShaderProgram('heightColor', [VertexShader('\n varying vec4 pos;\n void main() {\n gl_FrontColor = gl_Color;\n gl_BackColor = gl_Color;\n pos = gl_Vertex;\n gl_Position = ftransform();\n }\n '), FragmentShader('\n uniform float colorMap[9];\n varying vec4 pos;\n //out vec4 gl_FragColor; // only needed for later glsl versions\n //in vec4 gl_Color;\n void main() {\n vec4 color = gl_Color;\n color.x = colorMap[0] * (pos.z + colorMap[1]);\n if (colorMap[2] != 1.0)\n color.x = pow(color.x, colorMap[2]);\n color.x = color.x < 0. ? 0. : (color.x > 1. ? 1. : color.x);\n \n color.y = colorMap[3] * (pos.z + colorMap[4]);\n if (colorMap[5] != 1.0)\n color.y = pow(color.y, colorMap[5]);\n color.y = color.y < 0. ? 0. : (color.y > 1. ? 1. : color.y);\n \n color.z = colorMap[6] * (pos.z + colorMap[7]);\n if (colorMap[8] != 1.0)\n color.z = pow(color.z, colorMap[8]);\n color.z = color.z < 0. ? 0. : (color.z > 1. ? 1. : color.z);\n \n color.w = 1.0;\n gl_FragColor = color;\n }\n ')], uniforms={'colorMap': [1, 1, 1, 1, 0.5, 1, 1, 0, 1]}), ShaderProgram('pointSprite', [VertexShader('\n void main() {\n gl_FrontColor=gl_Color;\n gl_PointSize = gl_Normal.x;\n gl_Position = ftransform();\n } \n ')])] |
class PD(nn.Module):
def __init__(self, nf=64, groups=8):
super(PD, self).__init__()
self.L3_offset_conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.L3_dcnpack = DCN(nf, nf, 3, stride=1, padding=1, dilation=1, deformable_groups=groups, extra_offset_mask=True)
self.L2_offset_conv2 = nn.Conv2d((nf * 2), nf, 3, 1, 1, bias=True)
self.L2_offset_conv3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.L2_dcnpack = DCN(nf, nf, 3, stride=1, padding=1, dilation=1, deformable_groups=groups, extra_offset_mask=True)
self.L2_fea_conv = nn.Conv2d((nf * 2), nf, 3, 1, 1, bias=True)
self.L1_offset_conv2 = nn.Conv2d((nf * 2), nf, 3, 1, 1, bias=True)
self.L1_offset_conv3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.L1_dcnpack = DCN(nf, nf, 3, stride=1, padding=1, dilation=1, deformable_groups=groups, extra_offset_mask=True)
self.L1_fea_conv = nn.Conv2d((nf * 2), nf, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=False)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
def forward(self, nbr_fea_l):
L3_offset = nbr_fea_l[2]
L3_offset = self.lrelu(self.L3_offset_conv2(L3_offset))
L3_fea = self.lrelu(self.L3_dcnpack([nbr_fea_l[2], L3_offset]))
L3_f = L3_fea
L2_offset = nbr_fea_l[1]
L3_offset = self.upsample(L3_offset)
L2_offset = self.lrelu(self.L2_offset_conv2(torch.cat([L2_offset, (L3_offset * 2)], dim=1)))
L2_offset = self.lrelu(self.L2_offset_conv3(L2_offset))
L2_fea = self.L2_dcnpack([nbr_fea_l[1], L2_offset])
L3_fea = self.upsample(L3_fea)
L2_fea = self.lrelu(self.L2_fea_conv(torch.cat([L2_fea, L3_fea], dim=1)))
L2_f = L2_fea
L1_offset = nbr_fea_l[0]
L2_offset = self.upsample(L2_offset)
L1_offset = self.lrelu(self.L1_offset_conv2(torch.cat([L1_offset, (L2_offset * 2)], dim=1)))
L1_offset = self.lrelu(self.L1_offset_conv3(L1_offset))
L1_fea = self.L1_dcnpack([nbr_fea_l[0], L1_offset])
L2_fea = self.upsample(L2_fea)
L1_fea = self.L1_fea_conv(torch.cat([L1_fea, L2_fea], dim=1))
return (L1_fea, L2_f, L3_f) |
class TestSequenceGeneratorBase(unittest.TestCase):
def assertHypoTokens(self, hypo, tokens):
self.assertTensorEqual(hypo['tokens'], torch.LongTensor(tokens))
def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
self.assertAlmostEqual(hypo['positional_scores'], pos_scores)
self.assertEqual(pos_scores.numel(), hypo['tokens'].numel())
score = pos_scores.sum()
if normalized:
score /= (pos_scores.numel() ** lenpen)
self.assertLess(abs((score - hypo['score'])), 1e-06)
def assertAlmostEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), 'size mismatch')
self.assertLess((t1 - t2).abs().max(), 0.0001)
def assertTensorEqual(self, t1, t2):
self.assertEqual(t1.size(), t2.size(), 'size mismatch')
self.assertEqual(t1.ne(t2).long().sum(), 0) |
class Detect(nn.Module):
def __init__(self, num_classes=80, anchors=1, num_layers=3, inplace=True, head_layers=None, use_dfl=True, reg_max=16):
super().__init__()
assert (head_layers is not None)
self.nc = num_classes
self.no = (num_classes + 5)
self.nl = num_layers
if isinstance(anchors, (list, tuple)):
self.na = (len(anchors[0]) // 2)
else:
self.na = anchors
self.anchors = anchors
self.grid = ([torch.zeros(1)] * num_layers)
self.prior_prob = 0.01
self.inplace = inplace
stride = [8, 16, 32]
self.stride = torch.tensor(stride)
self.use_dfl = use_dfl
self.reg_max = reg_max
self.proj_conv = nn.Conv2d((self.reg_max + 1), 1, 1, bias=False)
self.grid_cell_offset = 0.5
self.grid_cell_size = 5.0
self.stems = nn.ModuleList()
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
self.cls_preds = nn.ModuleList()
self.reg_preds = nn.ModuleList()
for i in range(num_layers):
idx = (i * 5)
self.stems.append(head_layers[idx])
self.cls_convs.append(head_layers[(idx + 1)])
self.reg_convs.append(head_layers[(idx + 2)])
self.cls_preds.append(head_layers[(idx + 3)])
self.reg_preds.append(head_layers[(idx + 4)])
def initialize_biases(self):
for conv in self.cls_preds:
b = conv.bias.view((- 1))
b.data.fill_((- math.log(((1 - self.prior_prob) / self.prior_prob))))
conv.bias = torch.nn.Parameter(b.view((- 1)), requires_grad=True)
w = conv.weight
w.data.fill_(0.0)
conv.weight = torch.nn.Parameter(w, requires_grad=True)
for conv in self.reg_preds:
b = conv.bias.view((- 1))
b.data.fill_(1.0)
conv.bias = torch.nn.Parameter(b.view((- 1)), requires_grad=True)
w = conv.weight
w.data.fill_(0.0)
conv.weight = torch.nn.Parameter(w, requires_grad=True)
self.proj = nn.Parameter(torch.linspace(0, self.reg_max, (self.reg_max + 1)), requires_grad=False)
self.proj_conv.weight = nn.Parameter(self.proj.view([1, (self.reg_max + 1), 1, 1]).clone().detach(), requires_grad=False)
def forward(self, x, val_loss=None):
if self.training:
cls_score_list = []
reg_distri_list = []
for i in range(self.nl):
x[i] = self.stems[i](x[i])
cls_x = x[i]
reg_x = x[i]
cls_feat = self.cls_convs[i](cls_x)
cls_output = self.cls_preds[i](cls_feat)
reg_feat = self.reg_convs[i](reg_x)
reg_output = self.reg_preds[i](reg_feat)
cls_output = torch.sigmoid(cls_output)
cls_score_list.append(cls_output.flatten(2).permute((0, 2, 1)))
reg_distri_list.append(reg_output.flatten(2).permute((0, 2, 1)))
cls_score_list = torch.cat(cls_score_list, axis=1)
reg_distri_list = torch.cat(reg_distri_list, axis=1)
return (x, cls_score_list, reg_distri_list)
else:
cls_score_list = []
reg_dist_list = []
(anchor_points, stride_tensor) = generate_anchors(x, self.stride, self.grid_cell_size, self.grid_cell_offset, device=x[0].device, is_eval=True)
for i in range(self.nl):
(b, _, h, w) = x[i].shape
l = (h * w)
x[i] = self.stems[i](x[i])
cls_x = x[i]
reg_x = x[i]
cls_feat = self.cls_convs[i](cls_x)
cls_output = self.cls_preds[i](cls_feat)
reg_feat = self.reg_convs[i](reg_x)
reg_output = self.reg_preds[i](reg_feat)
if self.use_dfl:
reg_output = reg_output.reshape([(- 1), 4, (self.reg_max + 1), l]).permute(0, 2, 1, 3)
reg_output = self.proj_conv(F.softmax(reg_output, dim=1))
cls_output = torch.sigmoid(cls_output)
cls_score_list.append(cls_output.reshape([b, self.nc, l]))
reg_dist_list.append(reg_output.reshape([b, 4, l]))
cls_score_list = torch.cat(cls_score_list, axis=(- 1)).permute(0, 2, 1)
reg_dist_list = torch.cat(reg_dist_list, axis=(- 1)).permute(0, 2, 1)
pred_bboxes = dist2bbox(reg_dist_list, anchor_points, box_format='xywh')
pred_bboxes *= stride_tensor
return torch.cat([pred_bboxes, torch.ones((b, pred_bboxes.shape[1], 1), device=pred_bboxes.device, dtype=pred_bboxes.dtype), cls_score_list], axis=(- 1)) |
def fallback_version(root: _t.PathT, config: Configuration) -> (ScmVersion | None):
if (config.parentdir_prefix_version is not None):
(_, parent_name) = os.path.split(os.path.abspath(root))
if parent_name.startswith(config.parentdir_prefix_version):
version = tag_to_version(parent_name[len(config.parentdir_prefix_version):], config)
if (version is not None):
return meta(str(version), preformatted=True, config=config)
if (config.fallback_version is not None):
log.debug('FALLBACK %s', config.fallback_version)
return meta(config.fallback_version, preformatted=True, config=config)
return None |
('pypyr.venv.EnvBuilderWithExtraDeps')
def test_venv_create(mock_builder):
context = get_simple_context()
mocked_builder = mock_builder.return_value
mocked_builder.context = context
venv.run_step(Context({'venv': '/arb'}))
expected_path = str(Path('/arb').expanduser().resolve())
mocked_builder.create.assert_called_once_with(expected_path)
mocked_builder.upgrade_dependencies.assert_called_once_with(context)
mocked_builder.pip_install_extras.assert_not_called() |
((os.name == 'nt'), 'BNG Console does not work on Windows')
_model
def test_simulate_network_console():
Monomer('A')
Parameter('A_0', 1)
Initial(A(), A_0)
Parameter('k', 1)
Rule('degrade', (A() >> None), k)
with BngConsole(model) as bng:
bng.generate_network()
bng.action('simulate', method='ssa', t_end=20000, n_steps=100, netfile='pysb.net') |
def convert_file_size_to_str(total: int) -> str:
if (total < (1 << 10)):
size = '{:.2f} B'.format(total)
elif (total < (1 << 20)):
size = '{:.2f} K'.format((total / (1 << 10)))
elif (total < (1 << 30)):
size = '{:.2f} M'.format((total / (1 << 20)))
else:
size = '{:.2f} G'.format((total / (1 << 30)))
return size |
def train(train_loader, model, criterions, optimizer, epoch):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
loss_protest = AverageMeter()
loss_v = AverageMeter()
protest_acc = AverageMeter()
violence_mse = AverageMeter()
visattr_acc = AverageMeter()
end = time.time()
loss_history = []
for (i, sample) in enumerate(train_loader):
(input, target) = (sample['image'], sample['label'])
data_time.update((time.time() - end))
if args.cuda:
input = input.cuda()
for (k, v) in target.items():
target[k] = v.cuda()
target_var = {}
for (k, v) in target.items():
target_var[k] = Variable(v)
input_var = Variable(input)
output = model(input_var)
(losses, scores, N_protest) = calculate_loss(output, target_var, criterions)
optimizer.zero_grad()
loss = 0
for l in losses:
loss += l
loss.backward()
optimizer.step()
if N_protest:
loss_protest.update(losses[0].data[0], input.size(0))
loss_v.update((loss.data[0] - losses[0].data[0]), N_protest)
else:
loss_protest.update(losses[0].data[0], input.size(0))
loss_history.append(loss.data[0])
protest_acc.update(scores['protest_acc'], input.size(0))
violence_mse.update(scores['violence_mse'], N_protest)
visattr_acc.update(scores['visattr_acc'], N_protest)
batch_time.update((time.time() - end))
end = time.time()
if ((i % args.print_freq) == 0):
print('Epoch: [{0}][{1}/{2}] Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f}) Loss {loss_val:.3f} ({loss_avg:.3f}) Protest {protest_acc.val:.3f} ({protest_acc.avg:.3f}) Violence {violence_mse.val:.5f} ({violence_mse.avg:.5f}) Vis Attr {visattr_acc.val:.3f} ({visattr_acc.avg:.3f})'.format(epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, loss_val=(loss_protest.val + loss_v.val), loss_avg=(loss_protest.avg + loss_v.avg), protest_acc=protest_acc, violence_mse=violence_mse, visattr_acc=visattr_acc))
return loss_history |
class GPTNeoXJapaneseTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ['input_ids', 'attention_mask']
def __init__(self, vocab_file, emoji_file, unk_token='<|endoftext|>', pad_token='<|endoftext|>', bos_token='<|startoftext|>', eos_token='<|endoftext|>', do_clean_text=False, **kwargs):
super().__init__(unk_token=unk_token, pad_token=pad_token, bos_token=bos_token, eos_token=eos_token, do_clean_text=do_clean_text, **kwargs)
if (not os.path.isfile(vocab_file)):
raise ValueError(f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`")
if (not os.path.isfile(emoji_file)):
raise ValueError(f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`")
self.do_clean_text = do_clean_text
(self.vocab, self.raw_vocab, self.ids_to_tokens, self.emoji) = load_vocab_and_emoji(vocab_file, emoji_file)
self.subword_tokenizer = SubWordJapaneseTokenizer(vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji)
def vocab_size(self):
return len(self.raw_vocab)
def get_vocab(self):
return dict(self.raw_vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
return self.subword_tokenizer.tokenize(text, clean=self.do_clean_text)
def _convert_token_to_id(self, token):
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
return self.subword_tokenizer.convert_id_to_token(index)
def convert_tokens_to_string(self, tokens):
out_string = ''.join(tokens).strip()
return out_string
def _build_conversation_input_ids(self, conversation: 'Conversation') -> List[int]:
input_ids = []
for (is_user, text) in conversation.iter_texts():
input_ids.extend((self.encode(text, add_special_tokens=False) + [self.eos_token_id]))
if (len(input_ids) > self.model_max_length):
input_ids = input_ids[(- self.model_max_length):]
return input_ids
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(save_directory, (((filename_prefix + '-') if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']))
emoji_file = os.path.join(save_directory, (((filename_prefix + '-') if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']))
else:
vocab_file = ((((filename_prefix + '-') if filename_prefix else '') + save_directory) + VOCAB_FILES_NAMES['vocab_file'])
emoji_file = ((((filename_prefix + '-') if filename_prefix else '') + save_directory) + VOCAB_FILES_NAMES['emoji_file'])
with open(vocab_file, 'w', encoding='utf-8') as writer:
for (token_index, token) in self.ids_to_tokens.items():
if (index != token_index):
logger.warning(f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted!')
index = token_index
writer.write((','.join(token) + '\n'))
index += 1
with open(emoji_file, 'w', encoding='utf-8') as writer:
json.dump(self.emoji, writer)
return (vocab_file, emoji_file) |
def unpack(compressed):
uncompressed = np.zeros((compressed.shape[0] * 8), dtype=np.uint8)
uncompressed[::8] = ((compressed[:] >> 7) & 1)
uncompressed[1::8] = ((compressed[:] >> 6) & 1)
uncompressed[2::8] = ((compressed[:] >> 5) & 1)
uncompressed[3::8] = ((compressed[:] >> 4) & 1)
uncompressed[4::8] = ((compressed[:] >> 3) & 1)
uncompressed[5::8] = ((compressed[:] >> 2) & 1)
uncompressed[6::8] = ((compressed[:] >> 1) & 1)
uncompressed[7::8] = (compressed[:] & 1)
return uncompressed |
class IASIL2SO2BUFR(BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info, **kwargs):
super(IASIL2SO2BUFR, self).__init__(filename, filename_info, filetype_info)
(start_time, end_time) = self.get_start_end_date()
sc_id = self.get_attribute('satelliteIdentifier')
self.metadata = {}
self.metadata['start_time'] = start_time
self.metadata['end_time'] = end_time
self.metadata['SpacecraftName'] = data_center_dict[sc_id]
def start_time(self):
return self.metadata['start_time']
def end_time(self):
return self.metadata['end_time']
def platform_name(self):
return '{}'.format(self.metadata['SpacecraftName'])
def get_start_end_date(self):
fh = open(self.filename, 'rb')
i = 0
while True:
bufr = ec.codes_bufr_new_from_file(fh)
if (bufr is None):
break
ec.codes_set(bufr, 'unpack', 1)
year = ec.codes_get(bufr, 'year')
month = ec.codes_get(bufr, 'month')
day = ec.codes_get(bufr, 'day')
hour = ec.codes_get(bufr, 'hour')
minute = ec.codes_get(bufr, 'minute')
second = ec.codes_get(bufr, 'second')
obs_time = datetime(year=year, month=month, day=day, hour=hour, minute=minute, second=second)
if (i == 0):
start_time = obs_time
ec.codes_release(bufr)
i += 1
end_time = obs_time
fh.close()
return (start_time, end_time)
def get_attribute(self, key):
fh = open(self.filename, 'rb')
while True:
bufr = ec.codes_bufr_new_from_file(fh)
if (bufr is None):
break
ec.codes_set(bufr, 'unpack', 1)
attr = ec.codes_get(bufr, key)
ec.codes_release(bufr)
fh.close()
return attr
def get_array(self, key):
with open(self.filename, 'rb') as fh:
msgCount = 0
while True:
bufr = ec.codes_bufr_new_from_file(fh)
if (bufr is None):
break
ec.codes_set(bufr, 'unpack', 1)
values = ec.codes_get_array(bufr, key, float)
if (len(values) == 1):
values = np.repeat(values, 120)
if (msgCount == 0):
arr = da.from_array([values], chunks=CHUNK_SIZE)
else:
tmpArr = da.from_array([values], chunks=CHUNK_SIZE)
arr = da.concatenate((arr, tmpArr), axis=0)
msgCount = (msgCount + 1)
ec.codes_release(bufr)
if (arr.size == 1):
arr = arr[0]
return arr
def get_dataset(self, dataset_id, dataset_info):
arr = self.get_array(dataset_info['key'])
arr[(arr == dataset_info['fill_value'])] = np.nan
xarr = xr.DataArray(arr, dims=['y', 'x'], name=dataset_info['name'])
xarr.attrs['sensor'] = 'IASI'
xarr.attrs['platform_name'] = self.platform_name
xarr.attrs.update(dataset_info)
return xarr |
class SawyerCoffeePullV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'mug_pos': obs[3:6], 'unused_info': obs[6:]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_effort': 3})
action['delta_pos'] = move(o_d['hand_pos'], to_xyz=self._desired_pos(o_d), p=10.0)
action['grab_effort'] = self._grab_effort(o_d)
return action.array
def _desired_pos(o_d):
pos_curr = o_d['hand_pos']
pos_mug = o_d['mug_pos']
if (np.linalg.norm((pos_curr[:2] - pos_mug[:2])) > 0.06):
return (pos_mug + np.array([0.0, 0.0, 0.15]))
elif (abs((pos_curr[2] - pos_mug[2])) > 0.04):
return pos_mug
elif (pos_curr[1] > 0.7):
return np.array([0.5, 0.62, 0.1])
else:
return np.array([(pos_curr[0] - 0.1), 0.62, 0.1])
def _grab_effort(o_d):
pos_curr = o_d['hand_pos']
pos_mug = o_d['mug_pos']
if ((np.linalg.norm((pos_curr[:2] - pos_mug[:2])) > 0.06) or (abs((pos_curr[2] - pos_mug[2])) > 0.06)):
return (- 1.0)
else:
return 0.9 |
def get_packer_mapping(packers, task):
packer_mapping = {}
for (rec_id, packer) in packers.items():
obj_keys = [task.sim_obj_id_to_obj_key[obj_key] for obj_key in list(packer.matches.keys())]
rec_key = task.sim_obj_id_to_obj_key[rec_id]
for obj_key in obj_keys:
packer_mapping[obj_key] = rec_key
return packer_mapping |
def evaluate(args, model, tokenizer, criterion, prefix=''):
eval_output_dir = args.output_dir
eval_dataset = load_examples(args, tokenizer, evaluate=True)
if ((not os.path.exists(eval_output_dir)) and (args.local_rank in [(- 1), 0])):
os.makedirs(eval_output_dir)
args.eval_batch_size = (args.per_gpu_eval_batch_size * max(1, args.n_gpu))
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate_fn)
if ((args.n_gpu > 1) and (not isinstance(model, nn.DataParallel))):
model = nn.DataParallel(model)
logger.info('***** Running evaluation {} *****'.format(prefix))
logger.info(' Num examples = %d', len(eval_dataset))
logger.info(' Batch size = %d', args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
for batch in tqdm(eval_dataloader, desc='Evaluating'):
model.eval()
batch = tuple((t.to(args.device) for t in batch))
with torch.no_grad():
batch = tuple((t.to(args.device) for t in batch))
labels = batch[5]
inputs = {'input_ids': batch[0], 'input_modal': batch[2], 'attention_mask': batch[1], 'modal_start_tokens': batch[3], 'modal_end_tokens': batch[4]}
outputs = model(**inputs)
logits = outputs[0]
tmp_eval_loss = criterion(logits, labels)
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if (preds is None):
preds = (torch.sigmoid(logits).detach().cpu().numpy() > 0.5)
out_label_ids = labels.detach().cpu().numpy()
else:
preds = np.append(preds, (torch.sigmoid(logits).detach().cpu().numpy() > 0.5), axis=0)
out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)
eval_loss = (eval_loss / nb_eval_steps)
result = {'loss': eval_loss, 'macro_f1': f1_score(out_label_ids, preds, average='macro'), 'micro_f1': f1_score(out_label_ids, preds, average='micro')}
output_eval_file = os.path.join(eval_output_dir, prefix, 'eval_results.txt')
with open(output_eval_file, 'w') as writer:
logger.info('***** Eval results {} *****'.format(prefix))
for key in sorted(result.keys()):
logger.info(' %s = %s', key, str(result[key]))
writer.write(('%s = %s\n' % (key, str(result[key]))))
return result |
class PluginMethods(PluginActions):
def register_function(self, function, inputs, parameters, outputs, name, description, input_descriptions=None, parameter_descriptions=None, output_descriptions=None, citations=None, deprecated=False, examples=None):
if (citations is None):
citations = ()
else:
citations = tuple(citations)
if (examples is None):
examples = {}
method = qiime2.sdk.Method._init(function, inputs, parameters, outputs, self._plugin_id, name, description, input_descriptions, parameter_descriptions, output_descriptions, citations, deprecated, examples)
self[method.id] = method |
class RateLimiterBackendTests():
def setUp(self):
self.allowance = 10
self.interval = 1
ratelimiter_factory = RateLimiterContextFactory(self.backend_factory, self.allowance, self.interval)
self.baseplate_observer = TestBaseplateObserver()
baseplate = Baseplate()
baseplate.register(self.baseplate_observer)
baseplate.add_to_context('ratelimiter', ratelimiter_factory)
self.context = baseplate.make_context_object()
self.server_span = baseplate.make_server_span(self.context, 'test')
def test_ratelimiter_consume(self):
user_id = str(uuid4())
with self.server_span:
self.context.ratelimiter.consume(user_id, amount=self.allowance)
def test_ratelimiter_exceeded(self):
user_id = str(uuid4())
with self.server_span:
with self.assertRaises(RateLimitExceededException):
self.context.ratelimiter.consume(user_id, amount=(self.allowance + 1))
def test_ratelimiter_resets(self):
user_id = str(uuid4())
with self.server_span:
self.context.ratelimiter.consume(user_id, amount=self.allowance)
sleep(self.interval)
self.context.ratelimiter.consume(user_id, amount=self.allowance) |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('path', help='Path to result .json file as produced by 3D evaluation script. Can be downloaded from the evaluation server for test set results.')
args = parser.parse_args()
if (not os.path.exists(args.path)):
raise Exception('Result file not found!')
data = prepare_data(args.path)
plot_data(data) |
class PytestArg():
def __init__(self, request: FixtureRequest) -> None:
self._request = request
def gethookrecorder(self, hook) -> 'HookRecorder':
hookrecorder = HookRecorder(hook._pm)
self._request.addfinalizer(hookrecorder.finish_recording)
return hookrecorder |
class StreamReset(Event):
def __init__(self):
self.stream_id = None
self.error_code = None
self.remote_reset = True
def __repr__(self):
return ('<StreamReset stream_id:%s, error_code:%s, remote_reset:%s>' % (self.stream_id, self.error_code, self.remote_reset)) |
class tensorboard_log_wrapper(progress_bar):
def __init__(self, wrapped_bar, tensorboard_logdir, args):
self.wrapped_bar = wrapped_bar
self.tensorboard_logdir = tensorboard_logdir
self.args = args
try:
from tensorboardX import SummaryWriter
self.SummaryWriter = SummaryWriter
self._writers = {}
except ImportError:
print('tensorboard or required dependencies not found, please see README for using tensorboard. (e.g. pip install tensorboardX)')
self.SummaryWriter = None
def _writer(self, key):
if (self.SummaryWriter is None):
return None
if (key not in self._writers):
self._writers[key] = self.SummaryWriter(os.path.join(self.tensorboard_logdir, key))
self._writers[key].add_text('args', str(vars(self.args)))
self._writers[key].add_text('sys.argv', ' '.join(sys.argv))
return self._writers[key]
def __iter__(self):
return iter(self.wrapped_bar)
def log(self, stats, tag='', step=None):
self._log_to_tensorboard(stats, tag, step)
self.wrapped_bar.log(stats, tag=tag, step=step)
def print(self, stats, tag='', step=None):
self._log_to_tensorboard(stats, tag, step)
self.wrapped_bar.print(stats, tag=tag, step=step)
def __exit__(self, *exc):
for writer in getattr(self, '_writers', {}).values():
writer.close()
return False
def _log_to_tensorboard(self, stats, tag='', step=None):
writer = self._writer(tag)
if (writer is None):
return
if (step is None):
step = stats['num_updates']
for key in (stats.keys() - {'num_updates'}):
if isinstance(stats[key], AverageMeter):
writer.add_scalar(key, stats[key].val, step)
elif isinstance(stats[key], Number):
writer.add_scalar(key, stats[key], step) |
class TestParticleNumber(PropertyTest):
def setUp(self):
super().setUp()
num_spatial_orbitals = 4
self.prop = ParticleNumber(num_spatial_orbitals)
def test_second_q_ops(self):
ops = self.prop.second_q_ops()['ParticleNumber']
expected = {'+_0 -_0': 1.0, '+_1 -_1': 1.0, '+_2 -_2': 1.0, '+_3 -_3': 1.0, '+_4 -_4': 1.0, '+_5 -_5': 1.0, '+_6 -_6': 1.0, '+_7 -_7': 1.0}
self.assertEqual(dict(ops.items()), expected) |
class TestTransformerStretch(unittest.TestCase):
def test_default(self):
tfm = new_transformer()
tfm.stretch(1.1)
actual_args = tfm.effects
expected_args = ['stretch', '1.100000', '20.000000']
self.assertEqual(expected_args, actual_args)
actual_log = tfm.effects_log
expected_log = ['stretch']
self.assertEqual(expected_log, actual_log)
actual_res = tfm.build(INPUT_FILE, OUTPUT_FILE)
expected_res = True
self.assertEqual(expected_res, actual_res)
tfm_assert_array_to_file_output(INPUT_FILE, OUTPUT_FILE, tfm)
def test_factor_valid(self):
tfm = new_transformer()
tfm.stretch(0.7)
actual_args = tfm.effects
expected_args = ['stretch', '0.700000', '20.000000']
self.assertEqual(expected_args, actual_args)
actual_res = tfm.build(INPUT_FILE, OUTPUT_FILE)
expected_res = True
self.assertEqual(expected_res, actual_res)
tfm_assert_array_to_file_output(INPUT_FILE, OUTPUT_FILE, tfm)
def test_factor_extreme(self):
tfm = new_transformer()
tfm.stretch(0.2)
actual_args = tfm.effects
expected_args = ['stretch', '0.200000', '20.000000']
self.assertEqual(expected_args, actual_args)
actual_res = tfm.build(INPUT_FILE, OUTPUT_FILE)
expected_res = True
self.assertEqual(expected_res, actual_res)
tfm_assert_array_to_file_output(INPUT_FILE, OUTPUT_FILE, tfm)
def test_factor_invalid(self):
tfm = new_transformer()
with self.assertRaises(ValueError):
tfm.stretch((- 1))
def test_window_valid(self):
tfm = new_transformer()
tfm.stretch(0.99, window=10)
actual_args = tfm.effects
expected_args = ['stretch', '0.990000', '10.000000']
self.assertEqual(expected_args, actual_args)
actual_res = tfm.build(INPUT_FILE, OUTPUT_FILE)
expected_res = True
self.assertEqual(expected_res, actual_res)
tfm_assert_array_to_file_output(INPUT_FILE, OUTPUT_FILE, tfm)
def test_window_invalid(self):
tfm = new_transformer()
with self.assertRaises(ValueError):
tfm.stretch(0.99, window=0) |
def abort_current_continuation(args, env, cont):
from pycket.interpreter import return_multi_vals
if (not args):
raise SchemeException('abort-current-continuation: expected 1 or more args')
(tag, args) = (args[0], args[1:])
if (not isinstance(tag, values.W_ContinuationPromptTag)):
raise SchemeException('abort-current-continuation: expected prompt-tag for argument 0')
(prompt, frames) = find_continuation_prompt(tag, cont, direction='unwind')
if (prompt is None):
raise SchemeException('abort-current-continuation: no such prompt exists')
handler = prompt.handler
cont = prompt.get_previous_continuation()
assert (cont is not None)
if (handler is None):
if (not args):
raise SchemeException('abort-current-continuation: expected thunk as argument 1')
cont = Prompt(tag, None, env, cont)
handler = args[0]
args = []
if frames:
cont = call_handler_cont(handler, args, env, cont)
return unwind_frames(frames, env, cont)
return handler.call(args, env, cont) |
class InceptionResnetV2(nn.Module):
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0.0, output_stride=32, global_pool='avg'):
super(InceptionResnetV2, self).__init__()
self.drop_rate = drop_rate
self.num_classes = num_classes
self.num_features = 1536
assert (output_stride == 32)
self.conv2d_1a = BasicConv2d(in_chans, 32, kernel_size=3, stride=2)
self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.feature_info = [dict(num_chs=64, reduction=2, module='conv2d_2b')]
self.maxpool_3a = nn.MaxPool2d(3, stride=2)
self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
self.feature_info += [dict(num_chs=192, reduction=4, module='conv2d_4a')]
self.maxpool_5a = nn.MaxPool2d(3, stride=2)
self.mixed_5b = Mixed_5b()
self.repeat = nn.Sequential(Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17))
self.feature_info += [dict(num_chs=320, reduction=8, module='repeat')]
self.mixed_6a = Mixed_6a()
self.repeat_1 = nn.Sequential(Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1), Block17(scale=0.1))
self.feature_info += [dict(num_chs=1088, reduction=16, module='repeat_1')]
self.mixed_7a = Mixed_7a()
self.repeat_2 = nn.Sequential(Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2), Block8(scale=0.2))
self.block8 = Block8(no_relu=True)
self.conv2d_7b = BasicConv2d(2080, self.num_features, kernel_size=1, stride=1)
self.feature_info += [dict(num_chs=self.num_features, reduction=32, module='conv2d_7b')]
(self.global_pool, self.classif) = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
.ignore
def group_matcher(self, coarse=False):
module_map = {k: i for (i, (k, _)) in enumerate(flatten_modules(self.named_children(), prefix=()))}
module_map.pop(('classif',))
def _matcher(name):
if any([name.startswith(n) for n in ('conv2d_1', 'conv2d_2')]):
return 0
elif any([name.startswith(n) for n in ('conv2d_3', 'conv2d_4')]):
return 1
elif any([name.startswith(n) for n in ('block8', 'conv2d_7')]):
return (len(module_map) + 1)
else:
for k in module_map.keys():
if (k == tuple(name.split('.')[:len(k)])):
return module_map[k]
return float('inf')
return _matcher
.ignore
def set_grad_checkpointing(self, enable=True):
assert (not enable), 'checkpointing not supported'
.ignore
def get_classifier(self):
return self.classif
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
(self.global_pool, self.classif) = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.conv2d_1a(x)
x = self.conv2d_2a(x)
x = self.conv2d_2b(x)
x = self.maxpool_3a(x)
x = self.conv2d_3b(x)
x = self.conv2d_4a(x)
x = self.maxpool_5a(x)
x = self.mixed_5b(x)
x = self.repeat(x)
x = self.mixed_6a(x)
x = self.repeat_1(x)
x = self.mixed_7a(x)
x = self.repeat_2(x)
x = self.block8(x)
x = self.conv2d_7b(x)
return x
def forward_head(self, x, pre_logits: bool=False):
x = self.global_pool(x)
if (self.drop_rate > 0):
x = F.dropout(x, p=self.drop_rate, training=self.training)
return (x if pre_logits else self.classif(x))
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x |
class ThriftContextFactory(ContextFactory):
POOL_PREFIX = 'thrift_client_pool'
POOL_LABELS = ['thrift_pool']
max_connections_gauge = Gauge(f'{POOL_PREFIX}_max_size', 'Maximum number of connections in this thrift pool before blocking', POOL_LABELS)
active_connections_gauge = Gauge(f'{POOL_PREFIX}_active_connections', 'Number of connections currently in use in this thrift pool', POOL_LABELS)
def __init__(self, pool: ThriftConnectionPool, client_cls: Any):
self.pool = pool
self.client_cls = client_cls
self.proxy_cls = type('PooledClientProxy', (_PooledClientProxy,), {fn_name: _build_thrift_proxy_method(fn_name) for fn_name in _enumerate_service_methods(client_cls) if (not (fn_name.startswith('__') and fn_name.endswith('__')))})
def report_runtime_metrics(self, batch: metrics.Client) -> None:
pool_name = self.client_cls.__name__
self.max_connections_gauge.labels(pool_name).set(self.pool.size)
self.active_connections_gauge.labels(pool_name).set(self.pool.checkedout)
batch.gauge('pool.size').replace(self.pool.size)
batch.gauge('pool.in_use').replace(self.pool.checkedout)
def make_object_for_context(self, name: str, span: Span) -> '_PooledClientProxy':
return self.proxy_cls(self.client_cls, self.pool, span, name) |
class CellB(nn.Module):
def __init__(self, in_planes, out_planes, stride=1):
super(CellB, self).__init__()
self.stride = stride
self.sep_conv1 = SepConv(in_planes, out_planes, kernel_size=7, stride=stride)
self.sep_conv2 = SepConv(in_planes, out_planes, kernel_size=3, stride=stride)
self.sep_conv3 = SepConv(in_planes, out_planes, kernel_size=5, stride=stride)
if (stride == 2):
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(out_planes)
self.conv2 = nn.Conv2d((2 * out_planes), out_planes, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
def forward(self, x):
y1 = self.sep_conv1(x)
y2 = self.sep_conv2(x)
y3 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
if (self.stride == 2):
y3 = self.bn1(self.conv1(y3))
y4 = self.sep_conv3(x)
b1 = F.relu((y1 + y2))
b2 = F.relu((y3 + y4))
y = torch.cat([b1, b2], 1)
return F.relu(self.bn2(self.conv2(y))) |
class Model(object):
def __init__(self):
self.history = False
def present(self):
return (self.conf['state']['imu.rate'] * self.conf['past'])
def receive(self, name, value):
if ((name in self.conf['sensors']) and self.enabled):
self.inputs[name] = norm_sensor(name, value) |
class LassoXmlLexer(DelegatingLexer):
name = 'XML+Lasso'
aliases = ['xml+lasso']
version_added = '1.6'
alias_filenames = ['*.xml', '*.lasso', '*.lasso[89]', '*.incl', '*.inc', '*.las']
mimetypes = ['application/xml+lasso']
url = '
def __init__(self, **options):
super().__init__(XmlLexer, LassoLexer, **options)
def analyse_text(text):
rv = (LassoLexer.analyse_text(text) - 0.01)
if looks_like_xml(text):
rv += 0.4
return rv |
class ModelArguments():
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
config_name: Optional[str] = field(default=None, metadata={'help': 'Pretrained config name or path if not the same as model_name'})
tokenizer_name: Optional[str] = field(default=None, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
cache_dir: Optional[str] = field(default=None, metadata={'help': 'Where to store the pretrained models downloaded from huggingface.co'})
use_fast_tokenizer: bool = field(default=True, metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'})
model_revision: str = field(default='main', metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'})
use_auth_token: bool = field(default=False, metadata={'help': 'Will use the token generated when running `transformers-cli login` (necessary to use this script with private models).'}) |
def test_search__with_annotations(requests_mock):
requests_mock.get(f'{API_V1}/observations', json=SAMPLE_DATA['get_observation_with_ofvs'], status_code=200)
requests_mock.get(f'{API_V1}/controlled_terms', json=SAMPLE_DATA['get_controlled_terms'], status_code=200)
results = iNatClient().observations.search().all()
annotation = results[0].annotations[0]
assert isinstance(annotation, Annotation)
assert (annotation.controlled_attribute.label == 'Life Stage')
assert (annotation.controlled_value.label == 'Adult') |
def measure_peak_memory_cpu(function: Callable[([], None)], interval=0.5, device_idx=None) -> int:
def get_cpu_memory(process_id: int) -> int:
process = psutil.Process(process_id)
try:
meminfo_attr = ('memory_info' if hasattr(process, 'memory_info') else 'get_memory_info')
memory = getattr(process, meminfo_attr)()[0]
except psutil.AccessDenied:
raise ValueError('Error with Psutil.')
return memory
if (not is_psutil_available()):
logger.warning("Psutil not installed, we won't log CPU memory usage. Install Psutil (pip install psutil) to use CPU memory tracing.")
max_memory = 'N/A'
else:
class MemoryMeasureProcess(Process):
def __init__(self, process_id: int, child_connection: Connection, interval: float):
super().__init__()
self.process_id = process_id
self.interval = interval
self.connection = child_connection
self.num_measurements = 1
self.mem_usage = get_cpu_memory(self.process_id)
def run(self):
self.connection.send(0)
stop = False
while True:
self.mem_usage = max(self.mem_usage, get_cpu_memory(self.process_id))
self.num_measurements += 1
if stop:
break
stop = self.connection.poll(self.interval)
self.connection.send(self.mem_usage)
self.connection.send(self.num_measurements)
while True:
(child_connection, parent_connection) = Pipe()
mem_process = MemoryMeasureProcess(os.getpid(), child_connection, interval)
mem_process.start()
parent_connection.recv()
try:
function()
parent_connection.send(0)
max_memory = parent_connection.recv()
num_measurements = parent_connection.recv()
except Exception:
parent = psutil.Process(os.getpid())
for child in parent.children(recursive=True):
os.kill(child.pid, SIGKILL)
mem_process.join(0)
raise RuntimeError('Process killed. Error in Process')
mem_process.join((20 * interval))
if ((num_measurements > 4) or (interval < 1e-06)):
break
interval /= 10
return max_memory |
class BenchmarkMainComplex():
params = [['parallel', 'sequential'], ['basic', 'rule154', 'fig16'], ['local', 'redis']]
param_names = ['mode', 'network', 'cache']
timer = timeit.default_timer
number = 1
repeat = 1
timeout = 10000
def setup(self, mode, network, cache):
if (network == 'basic'):
self.network = examples.basic_network()
self.state = (0, 1, 1)
elif (network == 'rule154'):
self.network = examples.rule154_network()
self.state = (0, 1, 0, 1, 1)
elif (network == 'fig16'):
self.network = examples.fig16()
self.state = (1, 0, 0, 1, 1, 1, 0)
else:
raise ValueError(network)
self.default_config = copy.copy(config.__dict__)
if (mode == 'parallel'):
config.PARALLEL_CUT_EVALUATION = True
elif (mode == 'sequential'):
config.PARALLEL_CUT_EVALUATION = False
else:
raise ValueError(mode)
if (cache == 'local'):
config.REDIS_CACHE = False
elif (cache == 'redis'):
config.REDIS_CACHE = True
if (_cache.RedisConn().ping() is False):
raise NotImplementedError
else:
raise ValueError(cache)
config.CACHE_SIAS = False
def teardown(self, mode, network, cache):
config.__dict__.update(self.default_config)
def time_major_complex(self, mode, network, cache):
compute.major_complex(self.network, self.state) |
def evaluate(args):
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
model = BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization')
model.to(args.device)
model.eval()
symbols = {'BOS': tokenizer.vocab['[unused0]'], 'EOS': tokenizer.vocab['[unused1]'], 'PAD': tokenizer.vocab['[PAD]']}
if args.compute_rouge:
reference_summaries = []
generated_summaries = []
import nltk
import rouge
nltk.download('punkt')
rouge_evaluator = rouge.Rouge(metrics=['rouge-n', 'rouge-l'], max_n=2, limit_length=True, length_limit=args.beam_size, length_limit_type='words', apply_avg=True, apply_best=False, alpha=0.5, weight_factor=1.2, stemming=True)
args.result_path = ''
args.temp_dir = ''
data_iterator = build_data_iterator(args, tokenizer)
predictor = build_predictor(args, tokenizer, symbols, model)
logger.info('***** Running evaluation *****')
logger.info(' Number examples = %d', len(data_iterator.dataset))
logger.info(' Batch size = %d', args.batch_size)
logger.info('')
logger.info('***** Beam Search parameters *****')
logger.info(' Beam size = %d', args.beam_size)
logger.info(' Minimum length = %d', args.min_length)
logger.info(' Maximum length = %d', args.max_length)
logger.info(' Alpha (length penalty) = %.2f', args.alpha)
logger.info(' Trigrams %s be blocked', ('will' if args.block_trigram else 'will NOT'))
for batch in tqdm(data_iterator):
batch_data = predictor.translate_batch(batch)
translations = predictor.from_batch(batch_data)
summaries = [format_summary(t) for t in translations]
save_summaries(summaries, args.summaries_output_dir, batch.document_names)
if args.compute_rouge:
reference_summaries += batch.tgt_str
generated_summaries += summaries
if args.compute_rouge:
scores = rouge_evaluator.get_scores(generated_summaries, reference_summaries)
str_scores = format_rouge_scores(scores)
save_rouge_scores(str_scores)
print(str_scores) |
class GraphClassificationDataset(GraphDataset):
def __init__(self, graphs, labels):
super().__init__(graphs)
self.labels = labels
assert (len(graphs) == len(labels))
def __getitem__(self, index):
return (self.graphs[index], self.labels[index])
def collate_fn(batch):
(graphs, labels) = list(zip(*batch))
g = dgl.batch(graphs)
labels = torch.tensor(labels).long()
return (g, labels) |
class UnetBottleneck(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3, dilation=1, act_type='relu'):
super(UnetBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size, stride=1, padding=get_same_padding(kernel_size, dilation), dilation=dilation)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=kernel_size, stride=1, padding=get_same_padding(kernel_size, dilation), dilation=dilation)
self.act = (get_act(act_type=act_type) if act_type else None)
def forward(self, x):
out = self.act(self.conv1(x))
out = self.act(self.conv2(out))
return out |
def test_opdm_to_ohdm_mapping():
db = opdm_to_ohdm_mapping(6)
for dbe in db:
assert isinstance(dbe, DualBasisElement)
assert (set(dbe.primal_tensors_names) == {'ck', 'kc'})
if (len(dbe.primal_tensors_names) == 4):
assert np.allclose(dbe.primal_coeffs, 0.5)
assert np.isclose(dbe.dual_scalar, 0.0)
elif (len(dbe.primal_tensors_names) == 2):
assert np.allclose(dbe.primal_coeffs, 1.0)
assert np.isclose(dbe.dual_scalar, 1.0) |
def log(s, with_prefix=True, with_timestamp=True, color=None):
out = s
if with_prefix:
out = (_prefix_str + out)
if with_timestamp:
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y-%m-%d %H:%M:%S.%f %Z')
out = ('%s | %s' % (timestamp, out))
if (color is not None):
out = colorize(out, color)
if (not _log_tabular_only):
print(out)
for fd in list(_text_fds.values()):
fd.write((out + '\n'))
fd.flush()
sys.stdout.flush() |
class VAEAugExperiment(object):
def __init__(self, config):
self.config = config
self.device = self._get_device()
self.writer = SummaryWriter(os.path.join(self.config['save_dir'], self.config['vae_mode'], 'tensorboard'))
self.nt_xent_criterion = NTXentLoss(self.device, **config['loss'])
split_dir = os.path.join(self.config['base_dir'], 'splits.pkl')
self.data_dir = os.path.join(self.config['base_dir'], 'preprocessed')
with open(split_dir, 'rb') as f:
splits = pickle.load(f)
k = config['fold']
tr_keys = (splits[k]['train'] + splits[k]['val'])
self.val_keys = splits[k]['train']
self.train_loader = NumpyDataSet(self.data_dir, target_size=self.config['img_size'], batch_size=self.config['batch_size'], keys=tr_keys, do_reshuffle=True)
self.val_loader = NumpyDataSet(self.data_dir, target_size=self.config['img_size'], batch_size=self.config['val_batch_size'], keys=self.val_keys, do_reshuffle=True)
self.transforms = get_transforms(mode='vae')
print(len(self.train_loader))
if (self.config['vae_mode'] == 'beta'):
self.model = BetaVAE(in_channels=1, latent_dim=256).to(self.device)
elif (self.config['vae_mode'] == 'base'):
self.model = VanillaVAE(in_channels=1, latent_dim=256).to(self.device)
self.optimizer = torch.optim.Adam(self.model.parameters(), 0.0003, weight_decay=eval(self.config['weight_decay']))
self.save_folder = os.path.join('output_experiment', ('infer_vae' + str(datetime.now())[0:16]))
if (not os.path.exists(self.save_folder)):
os.mkdir(self.save_folder)
def _get_device(self):
device = ('cuda' if torch.cuda.is_available() else 'cpu')
print('Running on:', device)
return device
def train(self):
model_checkpoints_folder = os.path.join(self.writer.log_dir, 'checkpoints')
_save_config_file(model_checkpoints_folder)
n_iter = 0
valid_n_iter = 0
best_valid_loss = np.inf
scheduler = torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.95, last_epoch=(- 1))
for epoch_counter in range(self.config['epochs']):
print(('=====Training Epoch: %d =====' % epoch_counter))
for (i, data_batch) in enumerate(self.train_loader):
self.optimizer.zero_grad()
img = data_batch['data'][0].float().to(self.device)
label = data_batch['seg'][0].long().to(self.device)
new_data = dict()
new_data['data'] = data_batch['data'][0].numpy()
new_data = self.transforms(**new_data)
new_img = new_data['data'].float().to(self.device)
results = self.model(new_img, labels=label)
results[1] = img
train_loss = self.model.loss_function(*results, M_N=1, optimizer_idx=0, batch_idx=i)
loss = train_loss['loss']
if ((n_iter % self.config['log_every_n_steps']) == 0):
self.writer.add_scalar('train_loss', loss, global_step=n_iter)
print('Train:[{0}][{1}][{2}] loss: {loss:.4f}'.format(epoch_counter, i, len(self.train_loader), loss=loss.item()))
loss.backward()
self.optimizer.step()
n_iter += 1
torch.save(self.model.state_dict(), os.path.join(self.config['save_dir'], self.config['vae_mode'], 'b_{}_f{}_vae.pth'.format(self.config['batch_size'], self.config['fold'])))
if (epoch_counter >= 10):
scheduler.step()
self.writer.add_scalar('cosine_lr_decay', scheduler.get_lr()[0], global_step=n_iter)
def _load_pre_trained_weights(self, model):
try:
checkpoints_folder = os.path.join('./runs', self.config['fine_tune_from'], 'checkpoints')
state_dict = torch.load(os.path.join(checkpoints_folder, 'model.pth'))
model.load_state_dict(state_dict)
print('Loaded pre-trained model with success.')
except FileNotFoundError:
print('Pre-trained weights not found. Training from scratch.')
return model
def _validate(self, valid_loader):
with torch.no_grad():
self.model.eval()
valid_loss = 0.0
counter = 0
for (xis, xjs) in valid_loader:
xis = xis['data'][0].float().to(self.device)
xjs = xjs['data'][0].float().to(self.device)
loss = self._step(self.model, xis, xjs, counter)
valid_loss += loss.item()
counter += 1
valid_loss /= counter
return valid_loss
def load_checkpoint(self):
if (self.config['saved_model_path'] is None):
print('checkpoint_dir is empty, please provide directory to load checkpoint.')
exit(0)
else:
checkpoint = torch.load(self.config['saved_model_path'])
if ('model' not in checkpoint.keys()):
state_dict = checkpoint
else:
state_dict = checkpoint['model']
self.model.load_state_dict(state_dict, strict=False)
print('checkpoint state dict:', state_dict.keys())
print('model state dict:', self.model.state_dict().keys())
def infer(self):
self.load_checkpoint()
with torch.no_grad():
for k in range(len(self.val_keys)):
key = self.val_keys[k:(k + 1)]
data_loader = NumpyDataSet(self.data_dir, target_size=self.config['img_size'], batch_size=8, keys=key, do_reshuffle=False, mode='test')
feature_map = []
reconstruct_img = []
prediction = []
for (i, data_batch) in enumerate(data_loader):
data = data_batch['data'][0].float().to(self.device)
labels = data_batch['seg'][0].long().to(self.device)
slice_idx = data_batch['slice_idxs']
features = self.model(data, infer=True)
img = features[0]
mu = features[2]
logvar = features[3]
std = torch.exp((0.5 * logvar))
eps = torch.randn_like(std)
features = mu
features = F.normalize(features, p=2, dim=1)
for j in range(features.shape[0]):
feature_map.append(features[j].cpu().numpy())
reconstruct_img.append(img[j].cpu().numpy())
feature_map = np.stack(feature_map)
self.save_data(feature_map, key, 'features')
self.save_data(reconstruct_img, key, 'reconstruct')
def save_data(self, data, key, mode):
if (not os.path.exists(os.path.join(self.save_folder, mode))):
os.mkdir(os.path.join(self.save_folder, mode))
save_path = os.path.join(self.save_folder, ((mode + '_') + key[0]))
np.save(save_path, data) |
.parametrize(('metroids', 'stronger_metroids', 'bosses', 'artifacts'), [(False, False, True, 5), (False, True, False, 15), (True, False, True, 40), (True, True, True, 40)])
def test_msr_artifact_pool_should_throw_on_invalid_config(msr_game_description, metroids, stronger_metroids, bosses, artifacts):
configuration = MSRArtifactConfig(prefer_metroids=metroids, prefer_stronger_metroids=stronger_metroids, prefer_bosses=bosses, required_artifacts=artifacts)
with pytest.raises(InvalidConfiguration, match='More Metroid DNA than allowed!'):
artifact_pool(msr_game_description, configuration) |
def set_app_menu(app_menu_list):
class InternalMenu():
def __init__(self, title, parent):
self.m = AppKit.NSMenu.alloc().init()
self.item = AppKit.NSMenuItem.alloc().init()
self.item.setSubmenu_(self.m)
if (not isinstance(parent, self.__class__)):
self.m.setTitle_(title)
parent.addItem_(self.item)
else:
self.item.setTitle_(title)
parent.m.addItem_(self.item)
def action(self, title: str, action: Callable, command: (str | None)=None):
InternalAction(self, title, action, command)
return self
def separator(self):
self.m.addItem_(AppKit.NSMenuItem.separatorItem())
return self
def sub_menu(self, title: str):
return self.__class__(title, parent=self)
class InternalAction():
def __init__(self, parent: InternalMenu, title: str, action: callable, command=None):
self.action = action
s = selector(self._call_action, signature=b':')
if command:
item = parent.m.addItemWithTitle_action_keyEquivalent_(title, s, command)
else:
item = AppKit.NSMenuItem.alloc().init()
item.setAction_(s)
item.setTitle_(title)
parent.m.addItem_(item)
item.setTarget_(self)
def _call_action(self):
Thread(target=self.action).start()
def create_submenu(title, line_items, supermenu):
m = InternalMenu(title, parent=supermenu)
for menu_line_item in line_items:
if isinstance(menu_line_item, MenuSeparator):
m = m.separator()
elif isinstance(menu_line_item, MenuAction):
m = m.action(menu_line_item.title, menu_line_item.function)
elif isinstance(menu_line_item, Menu):
create_submenu(menu_line_item.title, menu_line_item.items, m)
os_bar_menu = BrowserView.app.mainMenu()
if (os_bar_menu is None):
os_bar_menu = AppKit.NSMenu.alloc().init()
BrowserView.app.setMainMenu_(os_bar_menu)
for app_menu in app_menu_list:
create_submenu(app_menu.title, app_menu.items, os_bar_menu) |
class PLMInputFeatures(InputFeatures):
def __init__(self, *_, perm_mask, target_mapping, **kwargs):
super().__init__(**kwargs)
self.perm_mask = perm_mask
self.target_mapping = target_mapping
def pretty_print(self, tokenizer):
return (((super().pretty_print(tokenizer) + '\n') + f'''perm_mask = {self.perm_mask}
''') + f'target_mapping = {self.target_mapping}') |
class TaskSetTimeHandler(TaskNewHandler):
.authenticated
async def get(self, taskid):
user = self.current_user
task = self.check_permission((await self.db.task.get(taskid, fields=('id', 'userid', 'tplid', 'disabled', 'note', 'ontime', 'ontimeflg', 'newontime'))), 'w')
newontime = json.loads(task['newontime'])
ontime = newontime
if ('mode' not in newontime):
ontime['mode'] = 'ontime'
else:
ontime = newontime
today_date = time.strftime('%Y-%m-%d', time.localtime())
(await self.render('task_setTime.html', task=task, ontime=ontime, today_date=today_date))
.authenticated
async def post(self, taskid):
log = u''
try:
envs = {}
for key in self.request.body_arguments:
envs[key] = self.get_body_arguments(key)
for env in envs.keys():
if ((envs[env][0] == u'true') or (envs[env][0] == u'false')):
envs[env] = (True if (envs[env][0] == u'true') else False)
else:
envs[env] = u'{0}'.format(envs[env][0])
async with self.db.transaction() as sql_session:
if envs['sw']:
c = cal()
if ('time' in envs):
if (len(envs['time'].split(':')) < 3):
envs['time'] = (envs['time'] + ':00')
tmp = c.calNextTs(envs)
if (tmp['r'] == 'True'):
(await self.db.task.mod(taskid, disabled=False, newontime=json.dumps(envs), next=tmp['ts'], sql_session=sql_session))
log = u',:{0}'.format(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(tmp['ts'])))
else:
raise Exception(tmp['r'])
else:
tmp = json.loads((await self.db.task.get(taskid, fields=('newontime',), sql_session=sql_session))['newontime'])
tmp['sw'] = False
(await self.db.task.mod(taskid, newontime=json.dumps(tmp), sql_session=sql_session))
except Exception as e:
if config.traceback_print:
traceback.print_exc()
(await self.render('utils_run_result.html', log=str(e), title=u'', flg='danger'))
logger_Web_Handler.error('TaskID: %s set Time failed! Reason: %s', taskid, str(e).replace('\\r\\n', '\r\n'))
return
(await self.render('utils_run_result.html', log=log, title=u'', flg='success'))
return |
class SelectOnRemove(MappingType):
MAPPING = {'prev': (QTabBar.SelectionBehavior.SelectLeftTab, 'Select the tab which came before the closed one (left in horizontal, above in vertical).'), 'next': (QTabBar.SelectionBehavior.SelectRightTab, 'Select the tab which came after the closed one (right in horizontal, below in vertical).'), 'last-used': (QTabBar.SelectionBehavior.SelectPreviousTab, 'Select the previously selected tab.')} |
class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer):
def __init__(self, args, params, fp32_optimizer, fp32_params):
super().__init__(args)
self.fp16_params = params
self.fp32_optimizer = fp32_optimizer
self.fp32_params = fp32_params
if (getattr(args, 'fp16_scale_window', None) is None):
if (len(args.update_freq) > 1):
raise ValueError('--fp16-scale-window must be given explicitly when using a custom --update-freq schedule')
scale_window = int((((2 ** 14) / args.distributed_world_size) / args.update_freq[0]))
else:
scale_window = args.fp16_scale_window
self.scaler = DynamicLossScaler(init_scale=args.fp16_init_scale, scale_window=scale_window, tolerance=args.fp16_scale_tolerance, threshold=args.threshold_loss_scale)
self.min_loss_scale = self.args.min_loss_scale
def build_optimizer(cls, args, params):
fp32_params = cls.build_fp32_params(params)
fp32_optimizer = optim.build_optimizer(args, [fp32_params])
return cls(args, params, fp32_optimizer, fp32_params)
def optimizer(self):
return self.fp32_optimizer.optimizer
def optimizer_config(self):
return self.fp32_optimizer.optimizer_config
def get_lr(self):
return self.fp32_optimizer.get_lr()
def set_lr(self, lr):
self.fp32_optimizer.set_lr(lr) |
def find_extremes(cluster_list, dataset, jaccard_threshold):
global _shared_dataset
_shared_dataset = dataset
extremes_list = []
f = partial(_find_cluster_extremes_shared, jaccard_threshold=jaccard_threshold)
with mp.Pool() as pool:
for extremes in tqdm(pool.imap_unordered(f, cluster_list), total=len(cluster_list)):
extremes_list.append(extremes)
return extremes_list |
class DefinitionWideFormat(sphinx_jsonschema.wide_format.WideFormat):
def _objecttype(self, schema):
rows = self._simpletype(schema)
rows.extend(self._objectproperties(schema, 'definition'))
rows.extend(self._objectproperties(schema, 'properties'))
rows.extend(self._objectproperties(schema, 'patternProperties'))
rows.extend(self._bool_or_object(schema, 'additionalProperties'))
rows.extend(self._kvpairs(schema, self.KV_OBJECT))
return rows
def _dispatch(self, schema, label=None):
rows = []
self.nesting += 1
if ('type' in schema):
if ('object' in schema['type']):
rows = self._objecttype(schema)
elif ('array' in schema['type']):
rows = self._arraytype(schema)
else:
rows = self._simpletype(schema)
elif ('description' in schema):
rows.append(self._line(self._cell(schema['description'])))
if ('$ref' in schema):
rows.append(self._line(self._cell(((':ref:`' + schema['$ref']) + '`').replace('#/definitions/', ''))))
for k in self.COMBINATORS:
if (k in schema):
items = []
for s in schema[k]:
items.extend(self._dispatch(s, self._cell('-')))
rows.extend(self._prepend(self._cell(k), items))
for k in self.SINGLEOBJECTS:
if (k in schema):
rows.extend(self._dispatch(schema[k], self._cell(k)))
rows.extend(self._objectproperties(schema, 'definitions'))
if (label is not None):
rows = self._prepend(label, rows)
self.nesting -= 1
return rows |
class SimclrInfoNCECriterion(nn.Module):
def __init__(self, buffer_params, temperature: float):
super(SimclrInfoNCECriterion, self).__init__()
self.use_gpu = (get_cuda_device_index() > (- 1))
self.temperature = temperature
self.num_pos = 2
self.buffer_params = buffer_params
self.criterion = nn.CrossEntropyLoss()
self.dist_rank = get_rank()
self.pos_mask = None
self.neg_mask = None
self.precompute_pos_neg_mask()
logging.info(f'Creating Info-NCE loss on Rank: {self.dist_rank}')
def precompute_pos_neg_mask(self):
total_images = self.buffer_params.effective_batch_size
world_size = self.buffer_params.world_size
batch_size = (total_images // world_size)
orig_images = (batch_size // self.num_pos)
rank = self.dist_rank
pos_mask = torch.zeros(batch_size, total_images)
neg_mask = torch.zeros(batch_size, total_images)
all_indices = np.arange(total_images)
pos_members = (orig_images * np.arange(self.num_pos))
orig_members = torch.arange(orig_images)
for anchor in np.arange(self.num_pos):
for img_idx in range(orig_images):
delete_inds = (((batch_size * rank) + img_idx) + pos_members)
neg_inds = torch.tensor(np.delete(all_indices, delete_inds)).long()
neg_mask[(((anchor * orig_images) + img_idx), neg_inds)] = 1
for pos in np.delete(np.arange(self.num_pos), anchor):
pos_inds = (((batch_size * rank) + (pos * orig_images)) + orig_members)
pos_mask[(torch.arange((anchor * orig_images), ((anchor + 1) * orig_images)).long(), pos_inds.long())] = 1
self.pos_mask = (pos_mask.cuda(non_blocking=True) if self.use_gpu else pos_mask)
self.neg_mask = (neg_mask.cuda(non_blocking=True) if self.use_gpu else neg_mask)
def forward(self, embedding: torch.Tensor):
assert (embedding.ndim == 2)
assert (embedding.shape[1] == int(self.buffer_params.embedding_dim))
batch_size = embedding.shape[0]
T = self.temperature
num_pos = self.num_pos
assert ((batch_size % num_pos) == 0), 'Batch size should be divisible by num_pos'
embeddings_buffer = self.gather_embeddings(embedding)
similarity = torch.exp((torch.mm(embedding, embeddings_buffer.t()) / T))
pos = torch.sum((similarity * self.pos_mask), 1)
neg = torch.sum((similarity * self.neg_mask), 1)
loss = (- torch.mean(torch.log((pos / (pos + neg)))))
return loss
def __repr__(self):
num_negatives = (self.buffer_params.effective_batch_size - 2)
T = self.temperature
num_pos = self.num_pos
repr_dict = {'name': self._get_name(), 'temperature': T, 'num_negatives': num_negatives, 'num_pos': num_pos, 'dist_rank': self.dist_rank}
return pprint.pformat(repr_dict, indent=2)
def gather_embeddings(embedding: torch.Tensor):
if (torch.distributed.is_available() and torch.distributed.is_initialized()):
embedding_gathered = gather_from_all(embedding)
else:
embedding_gathered = embedding
return embedding_gathered |
class ReadInputRegistersRequest(ReadRegistersRequestBase):
function_code = 4
function_code_name = 'read_input_registers'
def __init__(self, address=None, count=None, slave=0, **kwargs):
super().__init__(address, count, slave, **kwargs)
def execute(self, context):
if (not (1 <= self.count <= 125)):
return self.doException(merror.IllegalValue)
if (not context.validate(self.function_code, self.address, self.count)):
return self.doException(merror.IllegalAddress)
values = context.getValues(self.function_code, self.address, self.count)
if isinstance(values, ExceptionResponse):
return values
return ReadInputRegistersResponse(values) |
def deconv2D_layer(l0, name=None, filters=32, kernel_size=3, strides=2, padding='same', activation='relu', kernel_initializer='he_normal'):
l = Conv2DTranspose(filters=filters, name=name, kernel_size=kernel_size, strides=strides, padding=padding, activation=activation, kernel_initializer=kernel_initializer)(l0)
return l |
def test_create_poetry_with_local_config(fixture_dir: FixtureDirGetter) -> None:
poetry = Factory().create_poetry(fixture_dir('with_local_config'))
assert (not poetry.config.get('virtualenvs.in-project'))
assert (not poetry.config.get('virtualenvs.create'))
assert (not poetry.config.get('virtualenvs.options.always-copy'))
assert (not poetry.config.get('virtualenvs.options.no-pip'))
assert (not poetry.config.get('virtualenvs.options.no-setuptools'))
assert (not poetry.config.get('virtualenvs.options.system-site-packages')) |
def DFOM(pattern, ItemS):
count = 0
Nettree = [[[] for i in range(SeqNum)] for k in range(len(pattern))]
unit = [[] for k in range(len(pattern))]
for i in range(len(pattern)):
unit[i] = ItemS[str(pattern[i])]
for i in range(SeqNum):
for m in range(len(unit[0][i])):
bbb = 0
Nettree[0][i].append(unit[0][i][m])
for j in range(1, len(unit)):
n = 1
if (unit[j][i] == []):
bbb = 1
break
else:
for k in range(len(unit[j][i])):
if (Nettree[j][i] == []):
aaa = (- 1)
else:
aaa = Nettree[j][i][(- 1)]
if ((unit[j][i][k] > Nettree[(j - 1)][i][(- 1)]) and (unit[j][i][k] > aaa)):
Nettree[j][i].append(unit[j][i][k])
if (j == (len(unit) - 1)):
count += 1
n = 0
break
if n:
bbb = 1
break
if bbb:
break
return count |
def main():
try:
session = saga.Session()
print('Connecting...')
js = saga.job.Service('pbs+ssh://sierra.futuregrid.org', session=session)
print('CONNECTED')
jd = saga.job.Description()
jd.queue = 'batch'
jd.environment = {'RUNTIME': '5'}
jd.wall_time_limit = 1
jd.executable = '/bin/sleep'
jd.arguments = ['$RUNTIME']
sleepjob = js.create_job(jd)
print(('Job ID : %s' % sleepjob.id))
print(('Job State : %s' % sleepjob.state))
print('\n...starting job...\n')
sleepjob.run()
print(('Job ID : %s' % sleepjob.id))
print(('Job State : %s' % sleepjob.state))
print('\nListing active jobs: ')
for job in js.list():
print((' * %s' % job))
sleebjob_clone = js.get_job(sleepjob.id)
print('\n...waiting for job...\n')
sleebjob_clone.wait()
print(('Job State : %s' % sleebjob_clone.state))
print(('Exitcode : %s' % sleebjob_clone.exit_code))
print(('Exec. hosts : %s' % sleebjob_clone.execution_hosts))
print(('Create time : %s' % sleebjob_clone.created))
print(('Start time : %s' % sleebjob_clone.started))
print(('End time : %s' % sleebjob_clone.finished))
return 0
except saga.SagaException as ex:
print(('An exception occured: (%s) %s ' % (ex.type, str(ex))))
print((' \n*** Backtrace:\n %s' % ex.traceback))
return (- 1) |
def main():
parser = argparse.ArgumentParser(description='Convert model keys')
parser.add_argument('src', help='src detectron model path')
parser.add_argument('dst', help='save path')
parser.add_argument('depth', type=int, help='ResNet model depth')
args = parser.parse_args()
convert(args.src, args.dst, args.depth) |
.parametrize('const_shape', [(), (1,), (5,), (1, 5), (2, 5)])
.parametrize('op, np_op', [(pt.pow, np.power), (pt.add, np.add)])
def test_local_inline_composite_constants(op, np_op, const_shape):
const = np.full(shape=const_shape, fill_value=2.5).astype(config.floatX)
x = vector('x')
y = vector('y')
out = (pt.exp(op(x, const)) + y)
fn = pytensor.function([x, y], out, mode=get_default_mode().including('specialize', 'fusion'))
[node] = [node for node in fn.maker.fgraph.apply_nodes if isinstance(node.op, Elemwise)]
assert isinstance(node.op.scalar_op, Composite)
assert (len(node.inputs) == 2)
x_test_value = np.arange(5).astype(config.floatX)
y_test_value = np.ones(5).astype(config.floatX)
np.testing.assert_allclose(fn(x_test_value, y_test_value), (np.exp(np_op(x_test_value, const)) + y_test_value)) |
class Conv8(nn.Module):
def __init__(self):
super(Conv8, self).__init__()
builder = get_builder()
self.convs = nn.Sequential(builder.conv3x3(3, 64, first_layer=True), nn.ReLU(), builder.conv3x3(64, 64), nn.ReLU(), nn.MaxPool2d((2, 2)), builder.conv3x3(64, 128), nn.ReLU(), builder.conv3x3(128, 128), nn.ReLU(), nn.MaxPool2d((2, 2)), builder.conv3x3(128, 256), nn.ReLU(), builder.conv3x3(256, 256), nn.ReLU(), nn.MaxPool2d((2, 2)), builder.conv3x3(256, 512), nn.ReLU(), builder.conv3x3(512, 512), nn.ReLU(), nn.MaxPool2d((2, 2)))
self.linear = nn.Sequential(builder.conv1x1(((512 * 2) * 2), 256), nn.ReLU(), builder.conv1x1(256, 256), nn.ReLU(), builder.conv1x1(256, 10))
def forward(self, x):
out = self.convs(x)
out = out.view(out.size(0), ((512 * 2) * 2), 1, 1)
out = self.linear(out)
return out.squeeze() |
class ChannelPutSchema(BaseSchema):
token_address = AddressField(required=True)
partner_address = AddressField(required=True)
reveal_timeout = IntegerToStringField(missing=None)
settle_timeout = IntegerToStringField(missing=None)
total_deposit = IntegerToStringField(default=None, missing=None) |
class Effect7074(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Disintegrator Specialization')), 'damageMultiplier', (container.getModifiedItemAttr('damageMultiplierBonus') * level), **kwargs) |
class Effect5622(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Missile Launcher Torpedo')), 'speed', ship.getModifiedItemAttr('shipBonusMB'), skill='Minmatar Battleship', **kwargs) |
class RequiredImgAsset(RequiredAssetMixin, BaseRequiredImgAsset, BenefitFeature):
class Meta(BaseRequiredImgAsset.Meta, BenefitFeature.Meta):
verbose_name = 'Require Image'
verbose_name_plural = 'Require Images'
def __str__(self):
return f'Require image'
def as_form_field(self, **kwargs):
help_text = kwargs.pop('help_text', self.help_text)
label = kwargs.pop('label', self.label)
required = kwargs.pop('required', False)
return forms.ImageField(required=required, help_text=help_text, label=label, widget=forms.ClearableFileInput, **kwargs) |
class DomainGeometricParameters(BaseParameters):
def __init__(self, domain, main_param):
self.domain = domain
self.main_param = main_param
if (self.domain != 'separator'):
self.prim = ParticleGeometricParameters(domain, 'primary', main_param)
self.sec = ParticleGeometricParameters(domain, 'secondary', main_param)
self.phase_params = {'primary': self.prim, 'secondary': self.sec}
else:
self.phase_params = {}
def _set_parameters(self):
for phase in self.phase_params.values():
phase._set_parameters()
if (self.domain == 'separator'):
self.L = pybamm.Parameter('Separator thickness [m]')
self.b_e = pybamm.Parameter('Separator Bruggeman coefficient (electrolyte)')
return
Domain = self.domain.capitalize()
self.L_cc = pybamm.Parameter(f'{Domain} current collector thickness [m]')
self.L = pybamm.Parameter(f'{Domain} electrode thickness [m]')
self.L_tab = pybamm.Parameter(f'{Domain} tab width [m]')
self.centre_y_tab = pybamm.Parameter(f'{Domain} tab centre y-coordinate [m]')
self.centre_z_tab = pybamm.Parameter(f'{Domain} tab centre z-coordinate [m]')
self.A_tab = (self.L_tab * self.L_cc)
self.b_e = pybamm.Parameter(f'{Domain} electrode Bruggeman coefficient (electrolyte)')
self.b_s = pybamm.Parameter(f'{Domain} electrode Bruggeman coefficient (electrode)') |
def initial_wavefunction(particle):
return (((np.exp((((- 1) / (4 * ( ** 2))) * (((particle.x + (100 * A)) ** 2) + (1 * ((particle.y - 250) ** 2))))) / np.sqrt(((2 * np.pi) * ( ** 2)))) * np.exp(((p1_x0 * particle.x) * 1j))) + ((np.exp((((- 1) / (4 * ( ** 2))) * (((particle.x + (100 * A)) ** 2) + (1 * ((particle.y + 250) ** 2))))) / np.sqrt(((2 * np.pi) * ( ** 2)))) * np.exp(((p2_x0 * particle.x) * 1j)))) |
def plot(genotype, filename):
g = Digraph(format='pdf', edge_attr=dict(fontsize='20', fontname='times'), node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname='times'), engine='dot')
g.body.extend(['rankdir=LR'])
g.node('x_{t}', fillcolor='darkseagreen2')
g.node('h_{t-1}', fillcolor='darkseagreen2')
g.node('0', fillcolor='lightblue')
g.edge('x_{t}', '0', fillcolor='gray')
g.edge('h_{t-1}', '0', fillcolor='gray')
steps = len(genotype)
for i in range(1, (steps + 1)):
g.node(str(i), fillcolor='lightblue')
for (i, (op, j)) in enumerate(genotype):
g.edge(str(j), str((i + 1)), label=op, fillcolor='gray')
g.node('h_{t}', fillcolor='palegoldenrod')
for i in range(1, (steps + 1)):
g.edge(str(i), 'h_{t}', fillcolor='gray')
g.render(filename, view=False) |
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = (out_features or in_features)
hidden_features = (hidden_features or in_features)
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Conv2d):
trunc_normal_(m.weight, std=0.02)
if (m.bias is not None):
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x |
def render_notebook_cells(nbspec: NotebookSpec) -> NbCells:
return NbCells(title_cell=_md_nbnode('\n'.join(_get_title_lines(title=nbspec.title, mod=nbspec.module)), cqid='title_cell'), top_imports=_code_nbnode(_IMPORTS, cqid='top_imports'), gate_cells={gspec.cqid: _GateCells(md=_md_nbnode('\n'.join(get_markdown_docstring_lines(cls=gspec.gate_cls)), cqid=f'{gspec.cqid}.md'), py=_code_nbnode(_get_code_for_demoing(gspec), cqid=f'{gspec.cqid}.py')) for gspec in nbspec.gate_specs}) |
def get_extract_label(art_sents, abs_sents):
extracted = []
scores = []
indices = list(range(len(art_sents)))
for abst in abs_sents:
rouges = list(map(compute_rouge_l(reference=abst, mode='r'), art_sents))
ext = max(indices, key=(lambda i: rouges[i]))
indices.remove(ext)
extracted.append(ext)
scores.append(rouges[ext])
if (not indices):
break
return (extracted, scores) |
class Testuser():
def test_min_threshold_dist_from_shapefile(self):
f = examples.get_path('columbus.shp')
min_d = user.min_threshold_dist_from_shapefile(f)
assert (min_d == pytest.approx(0.))
def test_build_lattice_shapefile(self):
of = 'lattice.shp'
user.build_lattice_shapefile(20, 20, of)
w = Rook.from_shapefile(of)
assert (w.n == 400)
os.remove('lattice.dbf')
os.remove('lattice.shp')
os.remove('lattice.shx') |
class PFSTS_With_Ksappend(ParserTest):
def __init__(self, *args, **kwargs):
ParserTest.__init__(self, *args, **kwargs)
self.ks = '\nlang en_US\nkeyboard us\nautopart\n'
self.ksappend = '\ntimezone America/New_York\n'
def setUp(self):
ParserTest.setUp(self)
(handle, self._ksappendPath) = tempfile.mkstemp(prefix='ksappend-', text=True)
s = self.ksappend.encode('utf-8')
os.write(handle, s)
os.close(handle)
def tearDown(self):
ParserTest.tearDown(self)
os.unlink(self._ksappendPath)
def runTest(self):
processed = preprocessFromStringToString(((self.ks + '%ksappend ') + self._ksappendPath))
self.assertEqual(processed.decode(), (self.ks + self.ksappend)) |
class Fraction():
def __init__(self, fraction=None, chntext=None):
self.fraction = fraction
self.chntext = chntext
def chntext2fraction(self):
(denominator, numerator) = self.chntext.split('')
return ((chn2num(numerator) + '/') + chn2num(denominator))
def fraction2chntext(self):
(numerator, denominator) = self.fraction.split('/')
return ((num2chn(denominator) + '') + num2chn(numerator)) |
def evaluate_single_sub(sub_id):
truth = nib.load(os.path.joint('../data/preprocessed/HGG', sub_id, 'truth.nii.gz')).get_data()
prediction = nib.load(os.path.joint('prediction', sub_id, (sub_id + '.nii.gz'))).get_data()
masking_functions = (get_whole_tumor_mask, get_tumor_core_mask, get_enhancing_tumor_mask)
return [dice_coefficient(func(truth), func(prediction)) for func in masking_functions] |
def cast_tensor_type(inputs, src_type, dst_type):
if isinstance(inputs, nn.Module):
return inputs
elif isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isinstance(inputs, abc.Mapping):
return type(inputs)({k: cast_tensor_type(v, src_type, dst_type) for (k, v) in inputs.items()})
elif isinstance(inputs, abc.Iterable):
return type(inputs)((cast_tensor_type(item, src_type, dst_type) for item in inputs))
else:
return inputs |
def compute_KMM(Xsamples, sigma, noise, l_vec):
m = len(Xsamples)
Xbar = np.multiply(Xsamples, np.array(np.sqrt(([l_vec] * m))))
Qbar = np.array(([np.sum(np.multiply(Xbar, Xbar), axis=1)] * m)).T
distance = ((Qbar + Qbar.T) - (2 * np.dot(Xbar, Xbar.T)))
result = ((sigma * np.exp(((- 0.5) * distance))) + (np.eye(m) * (noise + (sigma * (10 ** (- 10))))))
return result |
def load_from_file(filename):
vehicles_loaded = 0
try:
with open(filename, 'r') as f:
lines = f.readlines()
for line in lines:
index = 0
index = line.split(',')
if (len(index) < 1):
continue
vehicletype = int(index[0])
spawn_x = float(index[1])
spawn_y = float(index[2])
spawn_z = float(index[3])
spawn_rot = float(index[4])
color_1 = int(index[5])
color_2 = int(index[6].split(';')[0])
if ((vehicletype < 400) or (vehicletype > 611)):
continue
if ((index[0] == (- 1)) or (index[1] == (- 1)) or (index[2] == (- 1)) or (index[3] == (- 1)) or (index[4] == (- 1))):
continue
add_static_vehicle_ex(vehicletype, spawn_x, spawn_y, spawn_z, spawn_rot, color_1, color_2, 1800)
vehicles_loaded += 1
f.close()
print(f'Loaded {int(vehicles_loaded)} vehicles from: {filename}')
except IOError as error:
print(error) |
def test_python_tags(wheelpath):
newname = tags(str(wheelpath), python_tags='py3')
assert (TESTWHEEL_NAME.replace('py2.py3', 'py3') == newname)
output_file = (wheelpath.parent / newname)
with WheelFile(str(output_file)) as f:
output = f.read((f.dist_info_path + '/WHEEL'))
assert (output == b'Wheel-Version: 1.0\nGenerator: bdist_wheel (0.30.0)\nRoot-Is-Purelib: false\nTag: py3-none-any\n\n')
output_file.unlink()
newname = tags(str(wheelpath), python_tags='py2.py3')
assert (TESTWHEEL_NAME == newname)
newname = tags(str(wheelpath), python_tags='+py4', remove=True)
assert (not wheelpath.exists())
assert (TESTWHEEL_NAME.replace('py2.py3', 'py2.py3.py4') == newname)
output_file = (wheelpath.parent / newname)
output_file.unlink() |
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