code stringlengths 17 6.64M |
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def collate_fn(batch):
res = defaultdict(list)
for d in batch:
for (k, v) in d.items():
res[k].append(v)
res['label'] = torch.stack(res['label'])
return res
|
def collate_fn_enrico(batch):
res = defaultdict(list)
for d in batch:
for (k, v) in d.items():
res[k].append(v)
res['label'] = torch.tensor(res['label'], dtype=torch.long)
return res
|
class EnricoImageDataset(torch.utils.data.Dataset):
def __init__(self, id_list_path, csv='../../metadata/screenclassification/design_topics.csv', class_map_file='../../metadata/screenclassification/class_map_enrico.json', img_folder=(os.environ['SM_CHANNEL_TRAINING'] if ('SM_CHANNEL_TRAINING' in os.environ) else... |
class CombinedImageDataset(torch.utils.data.IterableDataset):
def __init__(self, ds_list, prob_list):
super(CombinedImageDataset, self).__init__()
self.ds_list = ds_list
self.prob_list = prob_list
def __iter__(self):
while True:
dsi = choices(list(range(len(self.d... |
class SilverMultilabelImageDataset(torch.utils.data.Dataset):
def __init__(self, id_list_path=None, silver_id_list_path_ignores=None, K=150, P=1, csv='../../metadata/screenclassification/silver_webui-multi_topic.csv', img_folder=(os.environ['SM_CHANNEL_TRAINING'] if ('SM_CHANNEL_TRAINING' in os.environ) else '..... |
class SilverDataModule(pl.LightningDataModule):
def __init__(self, batch_size=16, num_workers=0, silver_id_list_path=None, silver_id_list_path_ignores=None, ra_num_ops=2, ra_magnitude=9, P=1, K=150, silver_csv='../../metadata/screenclassification/silver_webui-multi_topic.csv', img_folder='../../downloads/ds'):
... |
class EnricoDataModule(pl.LightningDataModule):
def __init__(self, batch_size=16, num_workers=4, img_size=128, ra_num_ops=(- 1), ra_magnitude=(- 1)):
super(EnricoDataModule, self).__init__()
self.batch_size = batch_size
self.num_workers = num_workers
self.train_dataset = EnricoIma... |
class UIScreenClassifier(pl.LightningModule):
def __init__(self, num_classes=20, dropout_block=0.0, dropout=0.2, lr=5e-05, soft_labels=True, stochastic_depth_p=0.2, arch='resnet50'):
super(UIScreenClassifier, self).__init__()
self.save_hyperparameters()
if ((arch == 'resnet50') or (arch =... |
class UIScreenSegmenter(pl.LightningModule):
def __init__(self, num_classes=20):
super(UIScreenSegmenter, self).__init__()
self.save_hyperparameters()
model = models.resnet50(pretrained=False, norm_layer=nn.InstanceNorm2d)
model.fc = nn.Linear(model.fc.in_features, num_classes)
... |
class StochasticBasicBlock(nn.Module):
def __init__(self, m, stochastic_depth_p=0.2, stochastic_depth_mode='row'):
super(StochasticBasicBlock, self).__init__()
self.m = m
self.sd = StochasticDepth(stochastic_depth_p, mode=stochastic_depth_mode)
def forward(self, x):
identity ... |
class StochasticBottleneck(nn.Module):
def __init__(self, m, stochastic_depth_p=0.2, stochastic_depth_mode='row'):
super(StochasticBottleneck, self).__init__()
self.m = m
self.sd = StochasticDepth(stochastic_depth_p, mode=stochastic_depth_mode)
def forward(self, x):
identity ... |
class CustomNormAndDropout(nn.Module):
def __init__(self, num_features, dropout):
super(CustomNormAndDropout, self).__init__()
self.norm = nn.InstanceNorm2d(num_features)
self.dropout = nn.Dropout2d(dropout)
def forward(self, x):
x = self.norm(x)
x = self.dropout(x)
... |
def replace_default_bn_with_custom(model, dropout=0.0):
for (child_name, child) in model.named_children():
if isinstance(child, nn.BatchNorm2d):
setattr(model, child_name, CustomNormAndDropout(child.num_features, dropout))
else:
replace_default_bn_with_custom(child, dropout... |
def replace_default_bn_with_in(model):
for (child_name, child) in model.named_children():
if isinstance(child, nn.BatchNorm2d):
setattr(model, child_name, nn.InstanceNorm2d(child.num_features))
else:
replace_default_bn_with_in(child)
|
def replace_res_blocks_with_stochastic(model, stochastic_depth_p=0.2, stochastic_depth_mode='row'):
all_blocks = []
def get_blocks(model, blocks):
for (child_name, child) in model.named_children():
if isinstance(child, BasicBlock):
blocks.append((child_name, StochasticBasi... |
class UIElementDetector(pl.LightningModule):
def __init__(self, num_classes=25, min_size=320, max_size=640, use_multi_head=True, lr=0.0001, val_weights=None, test_weights=None, arch='fcos'):
super(UIElementDetector, self).__init__()
self.save_hyperparameters()
if (arch == 'fcos'):
... |
def random_viewport_from_full(height, w, h):
h1 = int((random.random() * (h - height)))
h2 = (h1 + height)
viewport = (0, h1, w, h2)
return viewport
|
def random_viewport_pair_from_full(img_full, height_ratio):
img_pil = Image.open(img_full).convert('RGB')
(w, h) = img_pil.size
height = int((w * height_ratio))
viewport1 = random_viewport_from_full(height, w, h)
vh1 = viewport1[1]
delta = (int((random.random() * (2 * height))) - height)
v... |
class WebUISimilarityDataset(torch.utils.data.IterableDataset):
def __init__(self, split_file='../../downloads/train_split_web350k.json', root_dir='../../downloads/ds', domain_map_file='../../metadata/screensim/domain_map.json', duplicate_map_file='../../metadata/screensim/duplicate_map.json', device_name='iPhon... |
class WebUISimilarityDataModule(pl.LightningDataModule):
def __init__(self, batch_size=16, num_workers=4, split_file='../../downloads/train_split_web350k.json', root_dir='../../downloads/ds', domain_map_file='../../metadata/screensim/domain_map.json', duplicate_map_file='../../metadata/screensim/duplicate_map.js... |
class UIScreenEmbedder(pl.LightningModule):
def __init__(self, hidden_size=256, lr=5e-05, margin_pos=0.2, margin_neg=0.5, lambda_dann=1):
super(UIScreenEmbedder, self).__init__()
self.save_hyperparameters()
model = models.resnet18(pretrained=False)
replace_default_bn_with_in(model... |
def replace_default_bn_with_in(model):
for (child_name, child) in model.named_children():
if isinstance(child, nn.BatchNorm2d):
setattr(model, child_name, nn.InstanceNorm2d(child.num_features))
else:
replace_default_bn_with_in(child)
|
class DailyDialogParser():
def __init__(self, path, sos, eos, eou):
self.path = path
self.sos = sos
self.eos = eos
self.eou = eou
def get_dialogs(self):
train_dialogs = self.process_file((self.path + 'train.txt'))
validation_dialogs = self.process_file((self.p... |
class DPCollator():
def __init__(self, pad_token, reply_length=None):
self.pad_token = pad_token
self.reply_length = reply_length
def __call__(self, batch):
(contexts, replies) = zip(*batch)
padded_contexts = self.pad(contexts)
padded_replies = self.pad(replies, self.... |
class DPCorpus(object):
SOS = '<s>'
EOS = '</s>'
EOU = '</u>'
PAD = '<pad>'
UNK = '<unk>'
def __init__(self, dialog_parser=None, vocabulary_limit=None):
if (dialog_parser is None):
path = (os.path.dirname(os.path.realpath(__file__)) + '/daily_dialog/')
dialog_p... |
class DPDataLoader(DataLoader):
def __init__(self, dataset, batch_size=64):
if (dataset == None):
corpus = DPCorpus(vocabulary_limit=5000)
dataset = corpus.get_train_dataset(2, 5, 20)
collator = dataset.corpus.get_collator(reply_length=20)
super().__init__(dataset,... |
class DPDataset(Dataset):
def __init__(self, corpus, dialogs, context_size=2, min_reply_length=None, max_reply_length=None):
self.corpus = corpus
self.contexts = []
self.replies = []
for dialog in dialogs:
max_start_i = (len(dialog) - context_size)
for star... |
class Discriminator(nn.Module):
def __init__(self, embedding_dim, hidden_dim, vocab_size, max_seq_len, gpu=False, dropout=0.2, device='cpu'):
super(Discriminator, self).__init__()
self.hidden_dim = hidden_dim
self.embedding_dim = embedding_dim
self.max_seq_len = max_seq_len
... |
def greedy_match(fileone, filetwo, w2v):
res1 = greedy_score(fileone, filetwo, w2v)
res2 = greedy_score(filetwo, fileone, w2v)
res_sum = ((res1 + res2) / 2.0)
return (np.mean(res_sum), ((1.96 * np.std(res_sum)) / float(len(res_sum))), np.std(res_sum))
|
def greedy_score(fileone, filetwo, w2v):
f1 = open(fileone, 'r')
f2 = open(filetwo, 'r')
r1 = f1.readlines()
r2 = f2.readlines()
dim = w2v.layer1_size
scores = []
for i in range(len(r1)):
tokens1 = r1[i].strip().split(' ')
tokens2 = r2[i].strip().split(' ')
X = np.z... |
def extrema_score(fileone, filetwo, w2v):
f1 = open(fileone, 'r')
f2 = open(filetwo, 'r')
r1 = f1.readlines()
r2 = f2.readlines()
scores = []
for i in range(len(r1)):
tokens1 = r1[i].strip().split(' ')
tokens2 = r2[i].strip().split(' ')
X = []
for tok in tokens1... |
def average(fileone, filetwo, w2v):
f1 = open(fileone, 'r')
f2 = open(filetwo, 'r')
r1 = f1.readlines()
r2 = f2.readlines()
dim = w2v.layer1_size
scores = []
for i in range(len(r1)):
tokens1 = r1[i].strip().split(' ')
tokens2 = r2[i].strip().split(' ')
X = np.zeros(... |
def prepare_discriminator_data(pos_samples, neg_samples, gpu=False):
'\n Takes positive (target) samples, negative (generator) samples and prepares inp and target data for discriminator.\n\n Inputs: pos_samples, neg_samples\n - pos_samples: pos_size x seq_len\n - neg_samples: neg_size x seq_le... |
def load_data(path='dataset.pickle'):
'\n Load data set\n '
if (not os.path.isfile(path)):
corpus = DPCorpus(vocabulary_limit=VOCAB_SIZE)
train_dataset = corpus.get_train_dataset(min_reply_length=MIN_SEQ_LEN, max_reply_length=MAX_SEQ_LEN)
with open(path, 'wb') as handle:
... |
class ReplayMemory():
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
def push(self, transition):
if (len(self.memory) == self.capacity):
del self.memory[0]
self.memory.append(transition)
def push_batch(self, transition):
if (l... |
class Attention(nn.Module):
'\n Applies an attention mechanism on the output features from the decoder.\n\n .. math::\n \\begin{array}{ll}\n x = context*output \\\\\n attn = exp(x_i) / sum_j exp(x_j) \\\\\n output = \\tanh(w * (attn * context) + b * output)\n ... |
class BaseRNN(nn.Module):
"\n Applies a multi-layer RNN to an input sequence.\n Note:\n Do not use this class directly, use one of the sub classes.\n Args:\n vocab_size (int): size of the vocabulary\n max_len (int): maximum allowed length for the sequence to be processed\n hid... |
class EncoderRNN(BaseRNN):
'\n Applies a multi-layer RNN to an input sequence.\n\n Args:\n vocab_size (int): size of the vocabulary\n max_len (int): a maximum allowed length for the sequence to be processed\n hidden_size (int): the number of features in the hidden state `h`\n inp... |
class Seq2seq(nn.Module):
' Standard sequence-to-sequence architecture with configurable encoder\n and decoder.\n\n Args:\n encoder (EncoderRNN): object of EncoderRNN\n decoder (DecoderRNN): object of DecoderRNN\n decode_function (func, optional): function to generate symbols from outpu... |
class DailyDialogParser():
def __init__(self, path, sos, eos, eou):
self.path = path
self.sos = sos
self.eos = eos
self.eou = eou
def get_dialogs(self):
train_dialogs = self.process_file((self.path + 'train.txt'))
validation_dialogs = self.process_file((self.p... |
class DPCollator():
def __init__(self, pad_token, reply_length=None):
self.pad_token = pad_token
self.reply_length = reply_length
def __call__(self, batch):
(contexts, replies) = zip(*batch)
padded_contexts = self.pad(contexts)
padded_replies = self.pad(replies, self.... |
class DPCorpus(object):
SOS = '<s>'
EOS = '</s>'
EOU = '</u>'
PAD = '<pad>'
UNK = '<unk>'
def __init__(self, dialog_parser=None, vocabulary_limit=None):
if (dialog_parser is None):
path = (os.path.dirname(os.path.realpath(__file__)) + '/daily_dialog/')
dialog_p... |
class DPDataLoader(DataLoader):
def __init__(self, dataset, batch_size=64):
if (dataset == None):
corpus = DPCorpus(vocabulary_limit=5000)
dataset = corpus.get_train_dataset(2, 5, 20)
collator = dataset.corpus.get_collator(reply_length=20)
super().__init__(dataset,... |
class DPDataset(Dataset):
def __init__(self, corpus, dialogs, context_size=2, min_reply_length=None, max_reply_length=None):
self.corpus = corpus
self.contexts = []
self.replies = []
for dialog in dialogs:
max_start_i = (len(dialog) - context_size)
for star... |
class Discriminator(nn.Module):
def __init__(self, embedding_dim, hidden_dim, vocab_size, max_seq_len, gpu=False, dropout=0.2, device='cpu'):
super(Discriminator, self).__init__()
self.hidden_dim = hidden_dim
self.embedding_dim = embedding_dim
self.max_seq_len = max_seq_len
... |
def greedy_match(fileone, filetwo, w2v):
res1 = greedy_score(fileone, filetwo, w2v)
res2 = greedy_score(filetwo, fileone, w2v)
res_sum = ((res1 + res2) / 2.0)
return (np.mean(res_sum), ((1.96 * np.std(res_sum)) / float(len(res_sum))), np.std(res_sum))
|
def greedy_score(fileone, filetwo, w2v):
f1 = open(fileone, 'r')
f2 = open(filetwo, 'r')
r1 = f1.readlines()
r2 = f2.readlines()
dim = w2v.layer1_size
scores = []
for i in range(len(r1)):
tokens1 = r1[i].strip().split(' ')
tokens2 = r2[i].strip().split(' ')
X = np.z... |
def extrema_score(fileone, filetwo, w2v):
f1 = open(fileone, 'r')
f2 = open(filetwo, 'r')
r1 = f1.readlines()
r2 = f2.readlines()
scores = []
for i in range(len(r1)):
tokens1 = r1[i].strip().split(' ')
tokens2 = r2[i].strip().split(' ')
X = []
for tok in tokens1... |
def average(fileone, filetwo, w2v):
f1 = open(fileone, 'r')
f2 = open(filetwo, 'r')
r1 = f1.readlines()
r2 = f2.readlines()
dim = w2v.layer1_size
scores = []
for i in range(len(r1)):
tokens1 = r1[i].strip().split(' ')
tokens2 = r2[i].strip().split(' ')
X = np.zeros(... |
def prepare_discriminator_data(pos_samples, neg_samples, gpu=False):
'\n Takes positive (target) samples, negative (generator) samples and prepares inp and target data for discriminator.\n\n Inputs: pos_samples, neg_samples\n - pos_samples: pos_size x seq_len\n - neg_samples: neg_size x seq_le... |
def load_data(path='dataset.pickle'):
'\n Load data set\n '
if (not os.path.isfile(path)):
corpus = DPCorpus(vocabulary_limit=VOCAB_SIZE)
train_dataset = corpus.get_train_dataset(min_reply_length=MIN_SEQ_LEN, max_reply_length=MAX_SEQ_LEN)
with open(path, 'wb') as handle:
... |
class ReplayMemory():
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
def push(self, transition):
if (len(self.memory) == self.capacity):
del self.memory[0]
self.memory.append(transition)
def push_batch(self, transition):
if (l... |
class Attention(nn.Module):
'\n Applies an attention mechanism on the output features from the decoder.\n\n .. math::\n \\begin{array}{ll}\n x = context*output \\\\\n attn = exp(x_i) / sum_j exp(x_j) \\\\\n output = \\tanh(w * (attn * context) + b * output)\n ... |
class BaseRNN(nn.Module):
"\n Applies a multi-layer RNN to an input sequence.\n Note:\n Do not use this class directly, use one of the sub classes.\n Args:\n vocab_size (int): size of the vocabulary\n max_len (int): maximum allowed length for the sequence to be processed\n hid... |
class EncoderRNN(BaseRNN):
'\n Applies a multi-layer RNN to an input sequence.\n\n Args:\n vocab_size (int): size of the vocabulary\n max_len (int): a maximum allowed length for the sequence to be processed\n hidden_size (int): the number of features in the hidden state `h`\n inp... |
class Seq2seq(nn.Module):
' Standard sequence-to-sequence architecture with configurable encoder\n and decoder.\n\n Args:\n encoder (EncoderRNN): object of EncoderRNN\n decoder (DecoderRNN): object of DecoderRNN\n decode_function (func, optional): function to generate symbols from outpu... |
def parse_args():
parser = argparse.ArgumentParser(description='MMDet test (and eval) a model')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--work-dir', help='the directory to save the file containing evalua... |
def main():
args = parse_args()
assert (args.out or args.eval or args.format_only or args.show or args.show_dir), 'Please specify at least one operation (save/eval/format/show the results / save the results) with the argument "--out", "--eval", "--format-only", "--show" or "--show-dir"'
if (args.eval and ... |
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
parser.add_argument('--work-dir', help='the dir to save logs and models')
parser.add_argument('--resume-from', help='the checkpoint file to resume from')
... |
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if (args.cfg_options is not None):
cfg.merge_from_dict(args.cfg_options)
if cfg.get('custom_imports', None):
from mmcv.utils import import_modules_from_strings
import_modules_from_strings(**cfg['custom_imports']... |
class BMAML():
def __init__(self, dim_input, dim_output, dim_hidden=32, num_layers=4, num_particles=2, max_test_step=5):
self.dim_input = dim_input
self.dim_output = dim_output
self.dim_hidden = dim_hidden
self.num_layers = num_layers
self.num_particles = num_particles
... |
def train(model, dataset, saver, sess, config_str):
experiment_dir = ((FLAGS.logdir + '/') + config_str)
train_writer = tf.summary.FileWriter(experiment_dir, sess.graph)
print('Done initializing, starting training.')
num_iters_per_epoch = int((FLAGS.train_total_num_tasks / FLAGS.num_tasks))
if (no... |
def test(model, dataset, sess, inner_lr):
eval_valid_loss_list = []
for i in range(int((FLAGS.test_total_num_tasks / FLAGS.num_tasks))):
[follow_x, _, valid_x, follow_y, _, valid_y] = dataset.generate_batch(is_training=False, batch_idx=(i * FLAGS.num_tasks), inc_follow=True)
feed_in = OrderedD... |
def main():
random.seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
if (not os.path.exists(FLAGS.logdir)):
os.makedirs(FLAGS.logdir)
fname_args = []
if FLAGS.finite:
fname_args += [('train_total_num_tasks', 'SinusoidFinite')]
fname_args += [('t... |
class BNN(object):
def __init__(self, dim_input, dim_output, dim_hidden, num_layers, is_bnn=True):
self.dim_input = dim_input
self.dim_output = dim_output
self.dim_hidden = dim_hidden
self.num_layers = num_layers
self.is_bnn = is_bnn
def construct_network_weights(self... |
class EMAML():
def __init__(self, dim_input, dim_output, dim_hidden=32, num_layers=4, num_particles=2, max_test_step=5):
self.dim_input = dim_input
self.dim_output = dim_output
self.dim_hidden = dim_hidden
self.num_layers = num_layers
self.num_particles = num_particles
... |
def train(model, dataset, saver, sess, config_str):
experiment_dir = ((FLAGS.logdir + '/') + config_str)
train_writer = tf.summary.FileWriter(experiment_dir, sess.graph)
print('Done initializing, starting training.')
num_iters_per_epoch = int((FLAGS.train_total_num_tasks / FLAGS.num_tasks))
if (no... |
def test(model, dataset, sess, inner_lr):
eval_valid_loss_list = []
for i in range(int((FLAGS.test_total_num_tasks / FLAGS.num_tasks))):
[train_x, valid_x, train_y, valid_y] = dataset.generate_batch(is_training=False, batch_idx=(i * FLAGS.num_tasks))
feed_in = OrderedDict()
feed_in[mod... |
def main():
random.seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
if (not os.path.exists(FLAGS.logdir)):
os.makedirs(FLAGS.logdir)
fname_args = []
if FLAGS.finite:
fname_args += [('train_total_num_tasks', 'SinusoidFinite')]
fname_args += [('t... |
def pdist(tensor, metric='euclidean'):
assert isinstance(tensor, (tf.Variable, tf.Tensor)), 'tensor_utils.pdist: Input must be a `tensorflow.Tensor` instance.'
if (len(tensor.shape.as_list()) != 2):
raise ValueError('tensor_utils.pdist: A 2-d tensor must be passed.')
if (metric == 'euclidean'):
... |
def _is_vector(tensor):
return (len(tensor.shape.as_list()) == 1)
|
def median(tensor):
tensor_reshaped = tf.reshape(tensor, [(- 1)])
n_elements = tensor_reshaped.get_shape()[0]
sorted_tensor = tf.nn.top_k(tensor_reshaped, n_elements, sorted=True)
mid_index = (n_elements // 2)
if ((n_elements % 2) == 1):
return sorted_tensor.values[mid_index]
return ((... |
def squareform(tensor):
assert isinstance(tensor, tf.Tensor), 'tensor_utils.squareform: Input must be a `tensorflow.Tensor` instance.'
tensor_shape = tensor.shape.as_list()
n_elements = tensor_shape[0]
if _is_vector(tensor):
if (n_elements == 0):
return tf.zeros((1, 1), dtype=tenso... |
def get_images(paths, labels, nb_samples=None, shuffle=True):
if (nb_samples is not None):
sampler = (lambda x: random.sample(x, nb_samples))
else:
sampler = (lambda x: x)
images = [(i, os.path.join(path, image)) for (i, path) in zip(labels, paths) for image in sampler(os.listdir(path))]
... |
def clip_if_not_none(grad, min_value, max_value):
if (grad is None):
return grad
return tf.clip_by_value(grad, min_value, max_value)
|
def str2bool(v):
if (v.lower() in ('yes', 'true', 't', 'y', '1')):
return True
elif (v.lower() in ('no', 'false', 'f', 'n', '0')):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
|
def make_logdir(configs, fname_args=[]):
this_run_str = (time.strftime('%H%M%S_') + str(socket.gethostname()))
if is_git_dir():
this_run_str += ('_git' + git_hash_str())
for str_arg in fname_args:
if (str_arg in configs.keys()):
this_run_str += ((('_' + str_arg.title().replace(... |
def experiment_prefix_str(separator=',', hostname=False, git=True):
this_run_str = time.strftime('%y%m%d_%H%M%S')
if hostname:
this_run_str += str(socket.gethostname())
if (git and is_git_dir()):
this_run_str += (separator + str(git_hash_str()))
this_run_str = this_run_str.replace('-',... |
def experiment_string2(configs, fname_args=[], separator=','):
this_run_str = ''
for (org_arg_str, short_arg_str) in fname_args:
short_arg_str = (org_arg_str.title().replace('_', '') if (short_arg_str is None) else short_arg_str)
if (org_arg_str in configs.keys()):
this_run_str += ... |
def experiment_string(configs, fname_args=[], separator=','):
this_run_str = expr_prefix_str(configs)
for str_arg in fname_args:
if (str_arg in configs.keys()):
this_run_str += (((separator + str_arg.title().replace('_', '')) + '=') + str(configs[str_arg]))
else:
raise ... |
def is_git_dir():
from subprocess import call, STDOUT
if (call(['git', 'branch'], stderr=STDOUT, stdout=open(os.devnull, 'w')) != 0):
return False
else:
return True
|
def git_hash_str(hash_len=7):
import subprocess
hash_str = subprocess.check_output(['git', 'rev-parse', 'HEAD'])
return str(hash_str[:hash_len])
|
def multi_collate_fn(batch, samples_per_gpu=1):
'Puts each data field into a tensor/DataContainer with outer dimension\n batch size. This is mainly used in query_support dataloader. The main\n difference with the :func:`collate_fn` in mmcv is it can process\n list[list[DataContainer]].\n\n Extend def... |
def build_point_dataloader(dataset, samples_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, seed=None, **kwargs):
'Build PyTorch DataLoader.\n\n In distributed training, each GPU/process has a dataloader.\n In non-distributed training, there is only one dataloader for all GPUs.\n\n Args:\n... |
class PointGenerator(object):
def __init__(self, ann_file):
self.ann_file = ann_file
self.coco = COCO(ann_file)
self.seed = 0
def generate_points(self):
save_json = dict()
save_json['images'] = self.coco.dataset['images']
save_json['annotations'] = []
... |
def parse_args():
parser = argparse.ArgumentParser(description='MMDet test (and eval) a model')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--ceph', action='store_true', help='whether not to evaluate the che... |
def main():
args = parse_args()
assert (args.out or args.eval or args.format_only or args.show or args.show_dir), 'Please specify at least one operation (save/eval/format/show the results / save the results) with the argument "--out", "--eval", "--format-only", "--show" or "--show-dir"'
if (args.eval and ... |
def train_detector(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None):
logger = get_root_logger(log_level=cfg.log_level)
dataset = (dataset if isinstance(dataset, (list, tuple)) else [dataset])
if ('imgs_per_gpu' in cfg.data):
logger.warning('"imgs_per_gpu" is depre... |
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
parser.add_argument('--work-dir', help='the dir to save logs and models')
parser.add_argument('--resume-from', help='the checkpoint file to resume from')
... |
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if (args.cfg_options is not None):
for (k, v) in args.cfg_options.items():
args.cfg_options[k] = eval(v)
cfg.merge_from_dict(args.cfg_options)
if cfg.get('custom_imports', None):
from mmcv.utils impo... |
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--device', type=int, default=0, help='CUDA device id')
parser.add_arg... |
def main():
args = parse_args()
model = init_detector(args.config, args.checkpoint, device=torch.device('cuda', args.device))
camera = cv2.VideoCapture(args.camera_id)
print('Press "Esc", "q" or "Q" to exit.')
while True:
(ret_val, img) = camera.read()
result = inference_detector(m... |
def single_gpu_test(model, data_loader, show=False):
model.eval()
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(len(dataset))
for (i, data) in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=(not show), **data)
... |
def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
"Test model with multiple gpus.\n\n This method tests model with multiple gpus and collects the results\n under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'\n it encodes results to gpu tensors and use gpu com... |
def collect_results_cpu(result_part, size, tmpdir=None):
(rank, world_size) = get_dist_info()
if (tmpdir is None):
MAX_LEN = 512
dir_tensor = torch.full((MAX_LEN,), 32, dtype=torch.uint8, device='cuda')
if (rank == 0):
tmpdir = tempfile.mkdtemp()
tmpdir = torch.... |
def collect_results_gpu(result_part, size):
(rank, world_size) = get_dist_info()
part_tensor = torch.tensor(bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
shape_list = [shape_tensor.clone() for _ in range(world_size... |
def set_random_seed(seed, deterministic=False):
'Set random seed.\n\n Args:\n seed (int): Seed to be used.\n deterministic (bool): Whether to set the deterministic option for\n CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`\n to True and `torch.backends.cudnn.... |
def parse_losses(losses):
log_vars = OrderedDict()
for (loss_name, loss_value) in losses.items():
if isinstance(loss_value, torch.Tensor):
log_vars[loss_name] = loss_value.mean()
elif isinstance(loss_value, list):
log_vars[loss_name] = sum((_loss.mean() for _loss in los... |
def batch_processor(model, data, train_mode):
'Process a data batch.\n\n This method is required as an argument of Runner, which defines how to\n process a data batch and obtain proper outputs. The first 3 arguments of\n batch_processor are fixed.\n\n Args:\n model (nn.Module): A PyTorch model.... |
def train_detector(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None):
logger = get_root_logger(cfg.log_level)
dataset = (dataset if isinstance(dataset, (list, tuple)) else [dataset])
data_loaders = [build_dataloader(ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, len(... |
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