diff --git a/fluid/chinese_ner/README.md b/fluid/chinese_ner/README.md new file mode 100644 index 0000000000000000000000000000000000000000..11ad805f9108827002acadbf87da58b678fab4d6 --- /dev/null +++ b/fluid/chinese_ner/README.md @@ -0,0 +1,53 @@ +#使用ParallelExecutor的中文命名实体识别示例 + +以下是本例的简要目录结构及说明: + +```text +. +├── data # 存储运行本例所依赖的数据,从外部获取 +├── reader.py # 数据读取接口, 从外部获取 +├── README.md # 文档 +├── train.py # 训练脚本 +├── infer.py # 预测脚本 +``` + +##数据 +在data目录下,有两个文件夹,train_files中保存的是训练数据,test_files中保存的是测试数据,作为示例,在目录下我们各放置了两个文件,实际训练时,根据自己的实际需要将数据放置在对应目录,并根据数据格式,修改reader.py中的数据读取函数。 + +#训练 +修改train.py` 的 `main` 函数,指定数据路径,运行`python train.py`开始训练 +训练记录形如 +```txt +pass_id:0, time_cost:4.92960214615s +[Train] precision:0.000862136531076, recall:0.0059880239521, f1:0.00150726226363 +[Test] precision:0.000796178343949, recall:0.00335758254057, f1:0.00128713933283 +pass_id:1, time_cost:0.715255975723s +[Train] precision:0.00474094141551, recall:0.00762112139358, f1:0.00584551148225 +[Test] precision:0.0228873239437, recall:0.00727476217124, f1:0.0110403397028 +pass_id:2, time_cost:0.740842103958s +[Train] precision:0.0120967741935, recall:0.00163309744148, f1:0.00287769784173 +[Test] precision:0, recall:0.0, f1:0 +``` + +#预测 +修改 [infer.py](./infer.py) 的 `infer` 函数,指定:需要测试的模型的路径、测试数据、字典文件,预测标记文件的路径,运行`python infer.py`开始预测 +预测结果如下 +```txt +152804 O O +130048 O O +38862 10-B O +784 O O +1540 O O +4145 O O +2255 O O +0 O O +1279 O O +7793 O O +373 O O +1621 O O +815 O O +2 O O +247 24-B O +401 24-I O +``` +输出分为三列,以"\t"分割,第一列是输入的词语的序号,第二列是标准结果,第三列为标记结果。多条输入序列之间以空行分隔。 diff --git a/fluid/chinese_ner/data/label_dict b/fluid/chinese_ner/data/label_dict new file mode 100644 index 0000000000000000000000000000000000000000..7dfbc7c512a0c849c915ba78a367fe3cf547c351 --- /dev/null +++ b/fluid/chinese_ner/data/label_dict @@ -0,0 +1,49 @@ +24-B +24-I +27-B +27-I +20-B +20-I +21-B +21-I +22-B +22-I +23-B +23-I +28-B +28-I +29-B +29-I +12-B +12-I +11-B +11-I +10-B +10-I +13-B +13-I +38-B +38-I +14-B +14-I +16-B +16-I +33-B +33-I +18-B +18-I +31-B +31-I +30-B +30-I +37-B +37-I +36-B +36-I +35-B +35-I +19-B +19-I +32-B +32-I +O diff --git a/fluid/chinese_ner/data/test_files/test_part_1 b/fluid/chinese_ner/data/test_files/test_part_1 new file mode 100644 index 0000000000000000000000000000000000000000..233b942ffaa2a0442f4690203eca69798e17c40f --- /dev/null +++ b/fluid/chinese_ner/data/test_files/test_part_1 @@ -0,0 +1,500 @@ +;18528 45786 10718 1 348423 18528 45786 10718 408 2 1184 26 348423 0 370732 0 408 2 1184 0 13405 99 558667 2 1670 2 52 0 2 0 111;31 32 32 0 0 31 32 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17;39 40 40 0 0 39 40 40 33 34 33 0 0 0 0 0 33 34 33 0 21 22 0 0 33 0 0 0 0 0 31;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; +;88 19554 67 31745 423 561 6463 0 49680 0 537 0 224317 0 13851 0 88778 0 23 1256 2013 46 380362 4832 16141 25 116 0 2 0 88 2384 110 28 18 0 2 0 1774 884 224 23 13 199 712 729 1256;9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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34 0 33 0 33 0 0 0 1 2 0 0 0 0 0;21 21 0 55 56 56 56 56 0 55 0 21 0 33 0 1 2 0 21 22 33 34;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; +;222 13 29 13154 6280 149 909 2 134039 0 12656 2 3866 737 94 1690 1039 197;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;21 22 0 21 33 33 33 0 9 10 10 0 31 32 32 0 0 33;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; +;5541 3833 1043664 3928 88 360 5449 0 4296 35162 179 2 342 5 0;0 0 21 0 9 0 0 0 0 0 0 0 1 2 0;33 33 27 33 13 33 33 0 33 33 34 0 1 2 0;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; +;12 2046 11692 2631 4920 2414 81 7271;0 0 0 0 0 0 0 0;33 34 33 33 33 33 33 34;1 1 1 1 1 1 1 1; +;4603 21529 1630 347 1 961 110 5;7 8 0 0 0 1 2 2;11 12 33 33 0 1 2 2;1 1 1 1 1 1 1 1; +;3106 3 15739 294 2 2 1765 68 2 161 398 2 8676 192 5 0;0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 0;33 0 33 33 0 0 33 34 0 33 33 0 1 2 2 0;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; +;14 105 38 774 288 183001 583 1676 28 18 137 116 71 1 123 1 147 16 2 0 631 5;0 0 0 17 18 18 18 18 18 18 0 0 0 0 0 0 0 0 0 0 1 2;0 33 34 13 0 27 33 34 33 34 21 33 34 0 33 0 33 0 0 0 1 2;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; +;26324 27 4071 456 159 28 18 2 277;17 18 18 18 18 18 18 0 0;13 14 33 13 14 33 34 0 0;1 1 1 1 1 1 1 1 1; +;49 854 7016 48 5987 468 39 479 2675 1193 0 7 7 7 1 367 361 1 745 361 1 14 217 350 16;27 28 28 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;35 36 36 36 33 34 33 34 33 0 0 0 0 0 0 33 33 0 33 33 0 0 33 34 0;1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; diff --git a/fluid/chinese_ner/infer.py b/fluid/chinese_ner/infer.py new file mode 100644 index 0000000000000000000000000000000000000000..126cba896920a6d76b1f47fbe6b40f63a2395fad --- /dev/null +++ b/fluid/chinese_ner/infer.py @@ -0,0 +1,59 @@ +import numpy as np +import paddle.fluid as fluid +import paddle +import reader + + +def load_reverse_dict(dict_path): + return dict((idx, line.strip().split("\t")[0]) + for idx, line in enumerate(open(dict_path, "r").readlines())) + + +def infer(model_path, batch_size, test_data_file, target_file): + word = fluid.layers.data(name='word', shape=[1], dtype='int64', lod_level=1) + mention = fluid.layers.data( + name='mention', shape=[1], dtype='int64', lod_level=1) + target = fluid.layers.data( + name='target', shape=[1], dtype='int64', lod_level=1) + + label_reverse_dict = load_reverse_dict(target_file) + + test_data = paddle.batch( + reader.file_reader(test_data_file), batch_size=batch_size) + place = fluid.CPUPlace() + feeder = fluid.DataFeeder(feed_list=[word, mention, target], place=place) + exe = fluid.Executor(place) + + inference_scope = fluid.core.Scope() + with fluid.scope_guard(inference_scope): + [inference_program, feed_target_names, + fetch_targets] = fluid.io.load_inference_model(model_path, exe) + for data in test_data(): + crf_decode = exe.run(inference_program, + feed=feeder.feed(data), + fetch_list=fetch_targets, + return_numpy=False) + lod_info = (crf_decode[0].lod())[0] + np_data = np.array(crf_decode[0]) + assert len(data) == len(lod_info) - 1 + for sen_index in xrange(len(data)): + assert len(data[sen_index][0]) == lod_info[ + sen_index + 1] - lod_info[sen_index] + word_index = 0 + for tag_index in xrange(lod_info[sen_index], + lod_info[sen_index + 1]): + word = str(data[sen_index][0][word_index]) + gold_tag = label_reverse_dict[data[sen_index][2][ + word_index]] + tag = label_reverse_dict[np_data[tag_index][0]] + print word + "\t" + gold_tag + "\t" + tag + word_index += 1 + print "" + + +if __name__ == "__main__": + infer( + model_path="output/params_pass_0", + batch_size=6, + test_data_file="data/test_files", + target_file="data/label_dict") diff --git a/fluid/chinese_ner/reader.py b/fluid/chinese_ner/reader.py new file mode 100644 index 0000000000000000000000000000000000000000..9aa49c9feea19a46045dd099ee01211e21d72882 --- /dev/null +++ b/fluid/chinese_ner/reader.py @@ -0,0 +1,27 @@ +import os + + +def file_reader(file_dir): + def reader(): + files = os.listdir(file_dir) + for fi in files: + for line in open(file_dir + '/' + fi, 'r'): + line = line.strip() + features = line.split(";") + word_idx = [] + for item in features[1].strip().split(" "): + word_idx.append(int(item)) + target_idx = [] + for item in features[2].strip().split(" "): + label_index = int(item) + if label_index == 0: + label_index = 48 + else: + label_index -= 1 + target_idx.append(label_index) + mention_idx = [] + for item in features[3].strip().split(" "): + mention_idx.append(int(item)) + yield word_idx, mention_idx, target_idx, + + return reader diff --git a/fluid/chinese_ner/train.py b/fluid/chinese_ner/train.py new file mode 100644 index 0000000000000000000000000000000000000000..9d7d0f28392d21d388273b3432297db168b7167d --- /dev/null +++ b/fluid/chinese_ner/train.py @@ -0,0 +1,301 @@ +import os +import math +import time + +import numpy as np +import paddle +import paddle.fluid as fluid +from paddle.fluid.initializer import NormalInitializer + +import reader + + +def load_reverse_dict(dict_path): + return dict((idx, line.strip().split("\t")[0]) + for idx, line in enumerate(open(dict_path, "r").readlines())) + + +def to_lodtensor(data, place): + seq_lens = [len(seq) for seq in data] + cur_len = 0 + lod = [cur_len] + for l in seq_lens: + cur_len += l + lod.append(cur_len) + flattened_data = np.concatenate(data, axis=0).astype("int64") + flattened_data = flattened_data.reshape([len(flattened_data), 1]) + res = fluid.LoDTensor() + res.set(flattened_data, place) + res.set_lod([lod]) + return res + + +def ner_net(word_dict_len, label_dict_len): + IS_SPARSE = False + word_dim = 32 + mention_dict_len = 57 + mention_dim = 20 + grnn_hidden = 36 + emb_lr = 5 + init_bound = 0.1 + + def _net_conf(word, mark, target): + word_embedding = fluid.layers.embedding( + input=word, + size=[word_dict_len, word_dim], + dtype='float32', + is_sparse=IS_SPARSE, + param_attr=fluid.ParamAttr( + learning_rate=emb_lr, + name="word_emb", + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound))) + + mention_embedding = fluid.layers.embedding( + input=mention, + size=[mention_dict_len, mention_dim], + dtype='float32', + is_sparse=IS_SPARSE, + param_attr=fluid.ParamAttr( + learning_rate=emb_lr, + name="mention_emb", + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound))) + + word_embedding_r = fluid.layers.embedding( + input=word, + size=[word_dict_len, word_dim], + dtype='float32', + is_sparse=IS_SPARSE, + param_attr=fluid.ParamAttr( + learning_rate=emb_lr, + name="word_emb_r", + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound))) + + mention_embedding_r = fluid.layers.embedding( + input=mention, + size=[mention_dict_len, mention_dim], + dtype='float32', + is_sparse=IS_SPARSE, + param_attr=fluid.ParamAttr( + learning_rate=emb_lr, + name="mention_emb_r", + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound))) + + word_mention_vector = fluid.layers.concat( + input=[word_embedding, mention_embedding], axis=1) + + word_mention_vector_r = fluid.layers.concat( + input=[word_embedding_r, mention_embedding_r], axis=1) + + pre_gru = fluid.layers.fc( + input=word_mention_vector, + size=grnn_hidden * 3, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + gru = fluid.layers.dynamic_gru( + input=pre_gru, + size=grnn_hidden, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + pre_gru_r = fluid.layers.fc( + input=word_mention_vector_r, + size=grnn_hidden * 3, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + gru_r = fluid.layers.dynamic_gru( + input=pre_gru_r, + size=grnn_hidden, + is_reverse=True, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + gru_merged = fluid.layers.concat(input=[gru, gru_r], axis=1) + + emission = fluid.layers.fc( + size=label_dict_len, + input=gru_merged, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + crf_cost = fluid.layers.linear_chain_crf( + input=emission, + label=target, + param_attr=fluid.ParamAttr( + name='crfw', + learning_rate=0.2, )) + avg_cost = fluid.layers.mean(x=crf_cost) + return avg_cost, emission + + word = fluid.layers.data(name='word', shape=[1], dtype='int64', lod_level=1) + mention = fluid.layers.data( + name='mention', shape=[1], dtype='int64', lod_level=1) + target = fluid.layers.data( + name="target", shape=[1], dtype='int64', lod_level=1) + + avg_cost, emission = _net_conf(word, mention, target) + + return avg_cost, emission, word, mention, target + + +def test2(exe, chunk_evaluator, inference_program, test_data, place, + cur_fetch_list): + chunk_evaluator.reset() + for data in test_data(): + word = to_lodtensor(map(lambda x: x[0], data), place) + mention = to_lodtensor(map(lambda x: x[1], data), place) + target = to_lodtensor(map(lambda x: x[2], data), place) + result_list = exe.run( + inference_program, + feed={"word": word, + "mention": mention, + "target": target}, + fetch_list=cur_fetch_list) + number_infer = np.array(result_list[0]) + number_label = np.array(result_list[1]) + number_correct = np.array(result_list[2]) + chunk_evaluator.update(number_infer[0], number_label[0], + number_correct[0]) + return chunk_evaluator.eval() + + +def test(test_exe, chunk_evaluator, inference_program, test_data, place, + cur_fetch_list): + chunk_evaluator.reset() + for data in test_data(): + word = to_lodtensor(map(lambda x: x[0], data), place) + mention = to_lodtensor(map(lambda x: x[1], data), place) + target = to_lodtensor(map(lambda x: x[2], data), place) + result_list = test_exe.run( + fetch_list=cur_fetch_list, + feed={"word": word, + "mention": mention, + "target": target}) + number_infer = np.array(result_list[0]) + number_label = np.array(result_list[1]) + number_correct = np.array(result_list[2]) + chunk_evaluator.update(number_infer.sum(), + number_label.sum(), number_correct.sum()) + return chunk_evaluator.eval() + + +def main(train_data_file, test_data_file, model_save_dir, num_passes): + if not os.path.exists(model_save_dir): + os.mkdir(model_save_dir) + + BATCH_SIZE = 256 + word_dict_len = 1942563 + label_dict_len = 49 + + main = fluid.Program() + startup = fluid.Program() + with fluid.program_guard(main, startup): + avg_cost, feature_out, word, mention, target = ner_net(word_dict_len, + label_dict_len) + + sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3) + sgd_optimizer.minimize(avg_cost) + + crf_decode = fluid.layers.crf_decoding( + input=feature_out, param_attr=fluid.ParamAttr( + name='crfw', )) + + (precision, recall, f1_score, num_infer_chunks, num_label_chunks, + num_correct_chunks) = fluid.layers.chunk_eval( + input=crf_decode, + label=target, + chunk_scheme="IOB", + num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0))) + + chunk_evaluator = fluid.metrics.ChunkEvaluator() + + inference_program = fluid.default_main_program().clone() + with fluid.program_guard(inference_program): + inference_program = fluid.io.get_inference_program( + [num_infer_chunks, num_label_chunks, num_correct_chunks]) + + train_reader = paddle.batch( + paddle.reader.shuffle( + reader.file_reader(train_data_file), buf_size=2000000), + batch_size=BATCH_SIZE) + test_reader = paddle.batch( + paddle.reader.shuffle( + reader.file_reader(test_data_file), buf_size=2000000), + batch_size=BATCH_SIZE) + + place = fluid.CUDAPlace(0) + feeder = fluid.DataFeeder( + feed_list=[word, mention, target], place=place) + + exe = fluid.Executor(place) + + exe.run(startup) + train_exe = fluid.ParallelExecutor( + loss_name=avg_cost.name, use_cuda=True) + test_exe = fluid.ParallelExecutor( + use_cuda=True, + main_program=inference_program, + share_vars_from=train_exe) + batch_id = 0 + for pass_id in xrange(num_passes): + chunk_evaluator.reset() + train_reader_iter = train_reader() + start_time = time.time() + while True: + try: + cur_batch = next(train_reader_iter) + cost, nums_infer, nums_label, nums_correct = train_exe.run( + fetch_list=[ + avg_cost.name, num_infer_chunks.name, + num_label_chunks.name, num_correct_chunks.name + ], + feed=feeder.feed(cur_batch)) + chunk_evaluator.update( + np.array(nums_infer).sum(), + np.array(nums_label).sum(), + np.array(nums_correct).sum()) + cost_list = np.array(cost) + batch_id += 1 + except StopIteration: + break + end_time = time.time() + print("pass_id:" + str(pass_id) + ", time_cost:" + str( + end_time - start_time) + "s") + precision, recall, f1_score = chunk_evaluator.eval() + print("[Train] precision:" + str(precision) + ", recall:" + str( + recall) + ", f1:" + str(f1_score)) + p, r, f1 = test2( + exe, chunk_evaluator, inference_program, test_reader, place, + [num_infer_chunks, num_label_chunks, num_correct_chunks]) + print("[Test] precision:" + str(p) + ", recall:" + str(r) + ", f1:" + + str(f1)) + save_dirname = os.path.join(model_save_dir, + "params_pass_%d" % pass_id) + fluid.io.save_inference_model( + save_dirname, ['word', 'mention', 'target'], [crf_decode], exe) + + +if __name__ == "__main__": + main( + train_data_file="./data/train_files", + test_data_file="./data/test_files", + model_save_dir="./output", + num_passes=1000) diff --git a/fluid/object_detection/README.md b/fluid/object_detection/README.md index 67eccaed7303ad9bd6d5386729ae676eee52663f..9ef05eae9e564675dfe4506b0de5e65f7ac9a498 100644 --- a/fluid/object_detection/README.md +++ b/fluid/object_detection/README.md @@ -6,7 +6,7 @@ The minimum PaddlePaddle version needed for the code sample in this directory is ### Introduction -[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection is based on a feed-forward convolutional network. The early network is a standard convolutional architecture for image classification, such as VGG, ResNet, or MobileNet, which is als called base network. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861). +[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection is based on a feed-forward convolutional network. The early network is a standard convolutional architecture for image classification, such as VGG, ResNet, or MobileNet, which is also called base network. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861). ### Data Preparation @@ -52,30 +52,51 @@ Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https #### Train on PASCAL VOC - Train on one device (/GPU). ```python - env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --data='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/' + env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/' ``` - Train on multi devices (/GPUs). ```python - env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --data='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/' + env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/' ``` #### Train on MS-COCO - Train on one device (/GPU). ```python - env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --data='coco' --pretrained_model='pretrained/mobilenet_imagenet/' + env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --dataset='coco2014' --pretrained_model='pretrained/mobilenet_imagenet/' ``` - Train on multi devices (/GPUs). ```python - env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --data='coco' --pretrained_model='pretrained/mobilenet_imagenet/' + env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --dataset='coco2014' --pretrained_model='pretrained/mobilenet_imagenet/' ``` TBD ### Evaluate +You can evaluate your trained model in different metric like 11point, integral on both PASCAL VOC and COCO dataset. Moreover, we provide eval_coco_map.py which uses a COCO-specific mAP metric defined by [COCO committee](http://cocodataset.org/#detections-eval). To use this eval_coco_map.py, [cocoapi](https://github.com/cocodataset/cocoapi) is needed. +Install the cocoapi: +``` +# COCOAPI=/path/to/clone/cocoapi +git clone https://github.com/cocodataset/cocoapi.git $COCOAPI +cd $COCOAPI/PythonAPI +# Install into global site-packages +make install +# Alternatively, if you do not have permissions or prefer +# not to install the COCO API into global site-packages +python2 setup.py install --user +``` +Note we set the defualt test list to the dataset's test/val list, you can use your own test list by setting test_list args. + +#### Evaluate on PASCAL VOC +```python +env CUDA_VISIABLE_DEVICES=0 python eval.py --dataset='pascalvoc' --model_dir='train_pascal_model/90' --data_dir='data/pascalvoc' --test_list='test.txt' --ap_version='11point' +``` + +#### Evaluate on MS-COCO ```python -env CUDA_VISIABLE_DEVICES=0 python eval.py --model='model/90' --test_list='' +env CUDA_VISIABLE_DEVICES=0 python eval.py --dataset='coco2014' --nms_threshold=0.5 --model_dir='train_coco_model/40' --test_list='annotations/instances_minival2014.json' --ap_version='integral' +env CUDA_VISIABLE_DEVICES=0 python eval_coco_map.py --dataset='coco2017' --nms_threshold=0.5 --model_dir='train_coco_model/40' --test_list='annotations/instances_minival2017.json' ``` TBD @@ -83,8 +104,16 @@ TBD ### Infer and Visualize ```python -env CUDA_VISIABLE_DEVICES=0 python infer.py --batch_size=2 --model='model/90' --test_list='' +env CUDA_VISIABLE_DEVICES=0 python infer.py --model_dir='train_coco_model/20' --image_path='./data/coco/val2014/COCO_val2014_000000000139.jpg' ``` +Below is the examples after running python infer.py to inference and visualize the model result. +
+
+
+
+
+MobileNet-SSD300x300 Visualization Examples
+