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How to use test_window_size method in Airtest
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main.py
Source:main.py 
1# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import paddle15import argparse16from paddlevideo.utils import get_config17from paddlevideo.tasks import train_model, train_model_multigrid, test_model, train_dali18from paddlevideo.utils import get_dist_info19import numpy as np20import random21from paddlevideo.modeling.backbones.effigcn_init import EffigcnInit22def predefined_model_list():23 agcn_model_list = [24 {'cfg_path': 'configs/recognition/agcn_fold/agcn_fsd0.yaml',25 'weight_path': './output/AGCN_fold0/AGCN_best.pdparams',26 'test_window_size': [300, 350, 500]},27 {'cfg_path': 'configs/recognition/agcn_fold/agcn_fsd1.yaml',28 'weight_path': './output/AGCN_fold1/AGCN_best.pdparams',29 'test_window_size': [300, 350, 500]},30 {'cfg_path': 'configs/recognition/agcn_fold/agcn_fsd2.yaml',31 'weight_path': './output/AGCN_fold2/AGCN_best.pdparams',32 'test_window_size': [300, 350, 500]},33 {'cfg_path': 'configs/recognition/agcn_fold/agcn_fsd3.yaml',34 'weight_path': './output/AGCN_fold3/AGCN_best.pdparams',35 'test_window_size': [300, 350, 500]},36 {'cfg_path': 'configs/recognition/agcn_fold/agcn_fsd4.yaml',37 'weight_path': './output/AGCN_fold4/AGCN_best.pdparams',38 'test_window_size': [300, 350, 500]}39 ]40 ctrgcn_model_list = [41 {'cfg_path': 'configs/recognition/ctrgcn_fold/ctrgcn_fsd0.yaml',42 'weight_path': './output/CTRGCN_fold0/CTRGCN_best.pdparams',43 'test_window_size': [300, 350, 450]},44 {'cfg_path': 'configs/recognition/ctrgcn_fold/ctrgcn_fsd1.yaml',45 'weight_path': './output/CTRGCN_fold1/CTRGCN_best.pdparams',46 'test_window_size': [300, 350, 450]},47 {'cfg_path': 'configs/recognition/ctrgcn_fold/ctrgcn_fsd2.yaml',48 'weight_path': './output/CTRGCN_fold2/CTRGCN_best.pdparams',49 'test_window_size': [300, 350, 450]},50 {'cfg_path': 'configs/recognition/ctrgcn_fold/ctrgcn_fsd3.yaml',51 'weight_path': './output/CTRGCN_fold3/CTRGCN_best.pdparams',52 'test_window_size': [300, 350, 450]},53 {'cfg_path': 'configs/recognition/ctrgcn_fold/ctrgcn_fsd4.yaml',54 'weight_path': './output/CTRGCN_fold4/CTRGCN_best.pdparams',55 'test_window_size': [300, 350, 450]}56 ]57 ctrgcn2_model_list = [58 {'cfg_path': 'configs/recognition/ctrgcn2_fold/ctrgcn_fsd0.yaml',59 'weight_path': './output/CTRGCN2_fold0/CTRGCN_best.pdparams',60 'test_window_size': [350]},61 {'cfg_path': 'configs/recognition/ctrgcn2_fold/ctrgcn_fsd1.yaml',62 'weight_path': './output/CTRGCN2_fold1/CTRGCN_best.pdparams',63 'test_window_size': [350]},64 {'cfg_path': 'configs/recognition/ctrgcn2_fold/ctrgcn_fsd2.yaml',65 'weight_path': './output/CTRGCN2_fold2/CTRGCN_best.pdparams',66 'test_window_size': [350]},67 {'cfg_path': 'configs/recognition/ctrgcn2_fold/ctrgcn_fsd3.yaml',68 'weight_path': './output/CTRGCN2_fold3/CTRGCN_best.pdparams',69 'test_window_size': [350]},70 {'cfg_path': 'configs/recognition/ctrgcn2_fold/ctrgcn_fsd4.yaml',71 'weight_path': './output/CTRGCN2_fold4/CTRGCN_best.pdparams',72 'test_window_size': [350]}73 ]74 stgcn_model_list = [75 {'cfg_path': 'configs/recognition/stgcn_fold/stgcn_fsd0.yaml',76 'weight_path': './output/STGCN_fold0/STGCN_best.pdparams',77 'test_window_size': [350]},78 {'cfg_path': 'configs/recognition/stgcn_fold/stgcn_fsd1.yaml',79 'weight_path': './output/STGCN_fold1/STGCN_best.pdparams',80 'test_window_size': [350]},81 {'cfg_path': 'configs/recognition/stgcn_fold/stgcn_fsd2.yaml',82 'weight_path': './output/STGCN_fold2/STGCN_best.pdparams',83 'test_window_size': [350]},84 {'cfg_path': 'configs/recognition/stgcn_fold/stgcn_fsd3.yaml',85 'weight_path': './output/STGCN_fold3/STGCN_best.pdparams',86 'test_window_size': [350]},87 {'cfg_path': 'configs/recognition/stgcn_fold/stgcn_fsd4.yaml',88 'weight_path': './output/STGCN_fold4/STGCN_best.pdparams',89 'test_window_size': [350]},90 ]91 effigcn_model_list = [92 {'cfg_path': 'configs/recognition/effigcn_fold/effigcn_fsd0.yaml',93 'weight_path': './output/EFFGCN_fold0/EFFGCN_best.pdparams',94 'test_window_size': [500]},95 {'cfg_path': 'configs/recognition/effigcn_fold/effigcn_fsd1.yaml',96 'weight_path': './output/EFFGCN_fold1/EFFGCN_best.pdparams',97 'test_window_size': [500]},98 {'cfg_path': 'configs/recognition/effigcn_fold/effigcn_fsd2.yaml',99 'weight_path': './output/EFFGCN_fold2/EFFGCN_best.pdparams',100 'test_window_size': [500]},101 {'cfg_path': 'configs/recognition/effigcn_fold/effigcn_fsd3.yaml',102 'weight_path': './output/EFFGCN_fold3/EFFGCN_best.pdparams',103 'test_window_size': [500]},104 {'cfg_path': 'configs/recognition/effigcn_fold/effigcn_fsd4.yaml',105 'weight_path': './output/EFFGCN_fold4/EFFGCN_best.pdparams',106 'test_window_size': [500]},107 ]108 models = agcn_model_list + ctrgcn_model_list + ctrgcn2_model_list + stgcn_model_list + effigcn_model_list109 return models110def parse_args():111 parser = argparse.ArgumentParser("PaddleVideo train script")112 # parser.add_argument('-c',113 # '--config',114 # type=str,115 # default='configs/example.yaml',116 # help='config file path')117 parser.add_argument('-o',118 '--override',119 action='append',120 default=[],121 help='config options to be overridden')122 parser.add_argument('--test',123 action='store_true',124 help='whether to test a model')125 parser.add_argument('--train_dali',126 action='store_true',127 help='whether to use dali to speed up training')128 parser.add_argument('--multigrid',129 action='store_true',130 help='whether to use multigrid training')131 parser.add_argument('-w',132 '--weights',133 type=str,134 help='weights for finetuning or testing')135 parser.add_argument('--fleet',136 action='store_true',137 help='whether to use fleet run distributed training')138 parser.add_argument('--amp',139 action='store_true',140 help='whether to open amp training.')141 parser.add_argument(142 '--validate',143 action='store_true',144 help='whether to evaluate the checkpoint during training')145 parser.add_argument('-g', '--gpu', default="0", type=str)146 args = parser.parse_args()147 return args148def main():149 args = parse_args()150 # cfg = get_config(args.config, overrides=args.override)151 _, world_size = get_dist_info()152 parallel = world_size != 1153 if parallel:154 paddle.distributed.init_parallel_env()155 model_list = predefined_model_list()156 print("************************************************************")157 for model in model_list:158 print(model['cfg_path'])159 print("************************************************************")160 for model in model_list:161 np.random.seed(42)162 random.seed(2021)163 paddle.seed(102)164 cfg = get_config(model['cfg_path'], overrides=args.override)165 cfg['gpu'] = args.gpu166 print("*************" + model['cfg_path'] + "*******************")167 if (cfg.MODEL.backbone.name == 'EfficientGCN'):168 EffigcnInit(cfg) # åå§åEfficientGCNåæ°169 if args.test:170 test_model(cfg, weights=model['weight_path'], parallel=parallel, test_window_size=model['test_window_size'])171 else:172 train_model(cfg, weights=args.weights, parallel=parallel, validate=args.validate, use_fleet=args.fleet,173 amp=args.amp)174 # if args.test:175 # test_model(cfg, weights=args.weights, parallel=parallel)176 # elif args.train_dali:177 #178 # train_dali(cfg, weights=args.weights, parallel=parallel)179 # elif args.multigrid:180 # train_model_multigrid(cfg, world_size, validate=args.validate)181 # else:182 # train_model(cfg,183 # weights=args.weights,184 # parallel=parallel,185 # validate=args.validate,186 # use_fleet=args.fleet,187 # amp=args.amp)188if __name__ == '__main__':...
nightly_torch.py
Source:nightly_torch.py
1import sys2import time3#sys.path.append('../gpt-2/src/')4#import callable_gpt2 as gpt25#import model6#import encoder7#import sample8import torch9import numpy as np10import pandas as pd11from transformers import *12#tf.keras.backend.set_floatx('float64')13'''14error in tensorflow dtype init if tensorflow version = 2.0.0beta115pip uninstall tensorflow 16pip install tensorflow==2.0.017unhashable18tf.compat.v1.enable_tensor_equality()19'''20train_window_size = 40021test_window_size = 10022def pad_corpus_ori(sentences, pad_token, window_size):23 pad_sentences = []24 i = 025 for text in sentences:26 text = text.split()27 extended_text = text[:window_size-1] + [pad_token] * (window_size - len(text)-1)28 29 pad_sentences.append(extended_text)30 i += 131 return pad_sentences32def pad_corpus(sentences, start_token, end_token, pad_token, window_size):33 pad_sentences = []34 i = 035 for text in sentences:36 text = text.split()37 extended_text = text[:window_size-1] + [pad_token] * (window_size - len(text)-1) + [end_token]38 39 pad_sentences.append(extended_text)40 i += 141 return pad_sentences42def pad_corpus_simul(train_sentences, test_sentences, special_tokens, train_window_size, test_window_size):43 pad_sentences = []44 i = 045 for train_text, test_text in zip(train_sentences, test_sentences):46 train_text, test_text = train_text.split(), test_text.split()47 extended_text = [special_tokens['bos_token']] + train_text[:train_window_size-1] + [special_tokens['pad_token']] * (train_window_size - len(train_text)-1) + [special_tokens['cls_token']] + \48 test_text[:test_window_size-1] + [special_tokens['pad_token']] * (test_window_size - len(test_text)-1) + [special_tokens['eos_token']]49 50 pad_sentences.append(extended_text)51 i += 152 #print("length of post-padded text", len(pad_sentences[0]))53 return pad_sentences54def train(model, tokenizer, file_path, special_tokens):55 optimizer = AdamW(model.parameters())56 57 batch_size = 1058 reader = pd.read_json(file_path, precise_float=True, dtype=False, lines=True, chunksize=batch_size)59 train_steps = 060 step_start_time = time.time()61 training_time = [step_start_time]62 63 for section in reader:64 #print("section['normalizedBody']: \n", section['normalizedBody'].tolist(), len(section['normalizedBody'].tolist()))65 train_steps += 166 67 # get paragraph and summary68 train_words = section['normalizedBody'].tolist()69 test_words = section['summary'].tolist()70 #words = np.array(words)71 #print("words as numpy\n", words, "\n", words.shape)72 #print("concatted words", words)73 words = pad_corpus_simul(train_words, test_words, special_tokens, train_window_size, test_window_size)74 #test_words = pad_corpus(test_words, special_tokens['bos_token'], special_tokens['eos_token'], special_tokens['pad_token'], test_window_size)75 #train_words = pad_corpus_ori(train_words, special_tokens['pad_token'], train_window_size)76 #test_words = pad_corpus_ori(test_words, special_tokens['pad_token'], test_window_size)77 '''78 train_words = pad_corpus_ori(train_words, '', train_window_size)79 words = []80 for train, test in zip(train_words, test_words):81 words.append(train + test)82 '''83 words = [ tokenizer.encode(text) for text in words ]84 words = torch.tensor(words)85 test_words = pad_corpus_ori(test_words, special_tokens['pad_token'], test_window_size)86 test_words = [ tokenizer.encode(text) for text in test_words ]87 test_words = torch.tensor(test_words)88 #words = tf.reshape(words, [batch_size, -1])89 print("words as tensor\n", words, "\n", words.shape)90 91 outputs = model(words, labels=words) # last hidden states92 loss, logits = outputs[0], outputs[1]93 #logit = logit.numpy()94 predicted_index = torch.argmax(logits[0, -1, :])95 #loss = torch.keras.losses.sparse_categorical_crossentropy(words, logits, from_logits=True)96 #print("loss\n", loss, "\n", loss.shape)97 print("probs first\n", logits.shape)98 print("encoded prediction", predicted_index)99 print("decoded sentence first\n", tokenizer.decode(predicted_index))100 exit(0)101if __name__ == '__main__':102 # convert to pretrained to use transformers103 104 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')105 model = GPT2LMHeadModel.from_pretrained('gpt2')106 #print("loaded model", model)107 #add the special tokens into the vocabulary108 special_tokens = {'bos_token': '<bos>', 'cls_token': '<cls>', 'eos_token': '<eos>',109 'pad_token': '<pad>'}110 # tf transformers module not currently implemented resize_token_embeddings function :(111 tokenizer.add_special_tokens(special_tokens)112 model.resize_token_embeddings(len(tokenizer))113 114 #num_train_optimization_steps = len(self.train_dataset) * options['num_epochs'] // options['train_batchsize']115 #train(model, '../data/tldr_train80.jsonl', special_tokens)116 train(model, tokenizer, '../data/tldr-training-data.jsonl', special_tokens)...
out_score.py
Source:out_score.py
1import numpy as np2import csv3def get_model(model_name, power, test_window_size, power_window_size):4 """5 get score of model6 Args:7 model_name: str, we use model_name to find the folder8 power: ndarray, power of each fold to fuse9 test_window_size: list, the window_size we selected to fuse10 power_window_size: ndarray, power of each window_size to fuse11 Returns:12 score: score of model13 """14 out = np.zeros((634, 30), dtype='float32')15 power = power / power.sum()16 power_window_size = power_window_size / power_window_size.sum()17 for i in range(0, 5):18 for j in range(0, len(test_window_size)):19 path = "./output/%s_fold%s/score_%s.npy" % (model_name, i, test_window_size[j])20 print(path)21 data = np.load(path)22 out = out + data * power[i] * power_window_size[j]23 return out24def get_agcn():25 model_name = 'AGCN'26 power = np.array([1.5, 1, 1.5, 1, 2], dtype='float32')27 test_window_size = [300, 350, 500]28 power_window_size = np.array([1, 1, 1], dtype='float32')29 return get_model(model_name, power, test_window_size, power_window_size)30def get_ctrgcn():31 model_name = 'CTRGCN'32 power = np.array([1, 1, 2, 2, 3], dtype='float32')33 test_window_size = [300, 350, 450]34 power_window_size = np.array([1, 1, 1], dtype='float32')35 return get_model(model_name, power, test_window_size, power_window_size)36def get_ctrgcn2():37 model_name = 'CTRGCN2'38 power = np.array([1, 1, 2, 2, 3], dtype='float32')39 test_window_size = [350]40 power_window_size = np.array([1], dtype='float32')41 return get_model(model_name, power, test_window_size, power_window_size)42def get_stgcn():43 model_name = 'STGCN'44 power = np.array([1, 1, 2, 1, 2], dtype='float32')45 test_window_size = [350]46 power_window_size = np.array([1], dtype='float32')47 return get_model(model_name, power, test_window_size, power_window_size)48def get_effgcn():49 model_name = 'EFFGCN'50 power = np.array([1, 1, 2, 1, 3], dtype='float32')51 test_window_size = [500]52 power_window_size = np.array([1], dtype='float32')53 return get_model(model_name, power, test_window_size, power_window_size)54def get_all():55 out = np.zeros((634, 30), dtype='float32')56 data = []57 data.append(get_agcn())58 data.append(get_ctrgcn())59 data.append(get_ctrgcn2())60 data.append(get_stgcn())61 data.append(get_effgcn())62 power = np.array([2, 0.75, 1.5, 3.75, 2], dtype='float32')63 power = power / power.sum()64 for i in range(0, len(data)):65 out = out + data[i] * power[i]66 return out67def get_values_for_csv(data):68 values = []69 for i in range(0, data.shape[0]):70 j = np.argmax(data[i], axis=0)71 values.append((i, j))72 return values73def out_file(data, out_file):74 with open(75 out_file,76 'w',77 ) as fp:78 writer = csv.writer(fp)79 if out_file == "submission.csv":80 writer.writerow(['sample_index', 'predict_category'])81 writer.writerows(data)82if __name__ == "__main__":...
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