BIG Reorganize examples (#4213)
* Created using Colaboratory * [examples] reorganize files * remove run_tpu_glue.py as superseded by TPU support in Trainer * Bugfix: int, not tuple * move files around
This commit is contained in:
Julien Chaumond
23
examples/contrib/mm-imdb/README.md
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23
examples/contrib/mm-imdb/README.md
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## MM-IMDb
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Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/mm-imdb/run_mmimdb.py).
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[MM-IMDb](http://lisi1.unal.edu.co/mmimdb/) is a Multimodal dataset with around 26,000 movies including images, plots and other metadata.
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### Training on MM-IMDb
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```
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python run_mmimdb.py \
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--data_dir /path/to/mmimdb/dataset/ \
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--model_type bert \
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--model_name_or_path bert-base-uncased \
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--output_dir /path/to/save/dir/ \
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--do_train \
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--do_eval \
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--max_seq_len 512 \
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--gradient_accumulation_steps 20 \
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--num_image_embeds 3 \
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--num_train_epochs 100 \
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--patience 5
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```
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614
examples/contrib/mm-imdb/run_mmimdb.py
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614
examples/contrib/mm-imdb/run_mmimdb.py
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# coding=utf-8
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# Copyright (c) Facebook, Inc. and its affiliates.
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# Copyright (c) HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Finetuning the library models for multimodal multiclass prediction on MM-IMDB dataset."""
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import argparse
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import glob
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import json
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import logging
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import os
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import random
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import numpy as np
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import torch
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import torch.nn as nn
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from sklearn.metrics import f1_score
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from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
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from torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm, trange
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from transformers import (
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WEIGHTS_NAME,
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AdamW,
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AlbertConfig,
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AlbertModel,
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AlbertTokenizer,
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BertConfig,
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BertModel,
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BertTokenizer,
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DistilBertConfig,
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DistilBertModel,
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DistilBertTokenizer,
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MMBTConfig,
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MMBTForClassification,
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RobertaConfig,
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RobertaModel,
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RobertaTokenizer,
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XLMConfig,
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XLMModel,
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XLMTokenizer,
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XLNetConfig,
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XLNetModel,
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XLNetTokenizer,
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get_linear_schedule_with_warmup,
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)
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from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError:
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from tensorboardX import SummaryWriter
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logger = logging.getLogger(__name__)
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ALL_MODELS = sum(
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(
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tuple(conf.pretrained_config_archive_map.keys())
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for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)
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),
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(),
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)
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MODEL_CLASSES = {
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"bert": (BertConfig, BertModel, BertTokenizer),
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"xlnet": (XLNetConfig, XLNetModel, XLNetTokenizer),
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"xlm": (XLMConfig, XLMModel, XLMTokenizer),
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"roberta": (RobertaConfig, RobertaModel, RobertaTokenizer),
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"distilbert": (DistilBertConfig, DistilBertModel, DistilBertTokenizer),
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"albert": (AlbertConfig, AlbertModel, AlbertTokenizer),
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}
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def set_seed(args):
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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def train(args, train_dataset, model, tokenizer, criterion):
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""" Train the model """
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
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train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
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train_dataloader = DataLoader(
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train_dataset,
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sampler=train_sampler,
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batch_size=args.train_batch_size,
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collate_fn=collate_fn,
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num_workers=args.num_workers,
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)
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if args.max_steps > 0:
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t_total = args.max_steps
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args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
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else:
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t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": args.weight_decay,
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},
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{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = get_linear_schedule_with_warmup(
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optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
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)
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if args.fp16:
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try:
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from apex import amp
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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# multi-gpu training (should be after apex fp16 initialization)
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if args.n_gpu > 1:
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model = torch.nn.DataParallel(model)
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# Distributed training (should be after apex fp16 initialization)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(
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model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
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)
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# Train!
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_dataset))
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logger.info(" Num Epochs = %d", args.num_train_epochs)
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logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
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logger.info(
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" Total train batch size (w. parallel, distributed & accumulation) = %d",
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args.train_batch_size
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* args.gradient_accumulation_steps
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* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
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)
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logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
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logger.info(" Total optimization steps = %d", t_total)
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global_step = 0
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tr_loss, logging_loss = 0.0, 0.0
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best_f1, n_no_improve = 0, 0
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model.zero_grad()
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train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
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set_seed(args) # Added here for reproductibility
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for _ in train_iterator:
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epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
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for step, batch in enumerate(epoch_iterator):
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model.train()
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batch = tuple(t.to(args.device) for t in batch)
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labels = batch[5]
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inputs = {
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"input_ids": batch[0],
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"input_modal": batch[2],
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"attention_mask": batch[1],
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"modal_start_tokens": batch[3],
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"modal_end_tokens": batch[4],
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}
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outputs = model(**inputs)
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logits = outputs[0] # model outputs are always tuple in transformers (see doc)
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loss = criterion(logits, labels)
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel training
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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if args.fp16:
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torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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else:
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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optimizer.step()
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scheduler.step() # Update learning rate schedule
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model.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
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logs = {}
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if (
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args.local_rank == -1 and args.evaluate_during_training
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): # Only evaluate when single GPU otherwise metrics may not average well
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results = evaluate(args, model, tokenizer, criterion)
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for key, value in results.items():
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eval_key = "eval_{}".format(key)
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logs[eval_key] = value
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loss_scalar = (tr_loss - logging_loss) / args.logging_steps
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learning_rate_scalar = scheduler.get_lr()[0]
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logs["learning_rate"] = learning_rate_scalar
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logs["loss"] = loss_scalar
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logging_loss = tr_loss
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for key, value in logs.items():
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tb_writer.add_scalar(key, value, global_step)
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print(json.dumps({**logs, **{"step": global_step}}))
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if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
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# Save model checkpoint
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output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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model_to_save = (
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model.module if hasattr(model, "module") else model
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) # Take care of distributed/parallel training
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torch.save(model_to_save.state_dict(), os.path.join(output_dir, WEIGHTS_NAME))
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torch.save(args, os.path.join(output_dir, "training_args.bin"))
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logger.info("Saving model checkpoint to %s", output_dir)
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if args.max_steps > 0 and global_step > args.max_steps:
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epoch_iterator.close()
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break
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if args.max_steps > 0 and global_step > args.max_steps:
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train_iterator.close()
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break
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if args.local_rank == -1:
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results = evaluate(args, model, tokenizer, criterion)
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if results["micro_f1"] > best_f1:
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best_f1 = results["micro_f1"]
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n_no_improve = 0
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else:
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n_no_improve += 1
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if n_no_improve > args.patience:
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train_iterator.close()
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break
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if args.local_rank in [-1, 0]:
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tb_writer.close()
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return global_step, tr_loss / global_step
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def evaluate(args, model, tokenizer, criterion, prefix=""):
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# Loop to handle MNLI double evaluation (matched, mis-matched)
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eval_output_dir = args.output_dir
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eval_dataset = load_examples(args, tokenizer, evaluate=True)
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if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(eval_output_dir)
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args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
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# Note that DistributedSampler samples randomly
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eval_sampler = SequentialSampler(eval_dataset)
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eval_dataloader = DataLoader(
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eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate_fn
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)
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# multi-gpu eval
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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logger.info("***** Running evaluation {} *****".format(prefix))
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logger.info(" Num examples = %d", len(eval_dataset))
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logger.info(" Batch size = %d", args.eval_batch_size)
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eval_loss = 0.0
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nb_eval_steps = 0
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preds = None
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out_label_ids = None
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for batch in tqdm(eval_dataloader, desc="Evaluating"):
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model.eval()
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batch = tuple(t.to(args.device) for t in batch)
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with torch.no_grad():
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batch = tuple(t.to(args.device) for t in batch)
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labels = batch[5]
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inputs = {
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"input_ids": batch[0],
|
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"input_modal": batch[2],
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"attention_mask": batch[1],
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"modal_start_tokens": batch[3],
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"modal_end_tokens": batch[4],
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}
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outputs = model(**inputs)
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logits = outputs[0] # model outputs are always tuple in transformers (see doc)
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tmp_eval_loss = criterion(logits, labels)
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eval_loss += tmp_eval_loss.mean().item()
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nb_eval_steps += 1
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if preds is None:
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preds = torch.sigmoid(logits).detach().cpu().numpy() > 0.5
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out_label_ids = labels.detach().cpu().numpy()
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else:
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preds = np.append(preds, torch.sigmoid(logits).detach().cpu().numpy() > 0.5, axis=0)
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out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)
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eval_loss = eval_loss / nb_eval_steps
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result = {
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"loss": eval_loss,
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"macro_f1": f1_score(out_label_ids, preds, average="macro"),
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"micro_f1": f1_score(out_label_ids, preds, average="micro"),
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}
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output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
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with open(output_eval_file, "w") as writer:
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logger.info("***** Eval results {} *****".format(prefix))
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for key in sorted(result.keys()):
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logger.info(" %s = %s", key, str(result[key]))
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writer.write("%s = %s\n" % (key, str(result[key])))
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return result
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def load_examples(args, tokenizer, evaluate=False):
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path = os.path.join(args.data_dir, "dev.jsonl" if evaluate else "train.jsonl")
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transforms = get_image_transforms()
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labels = get_mmimdb_labels()
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dataset = JsonlDataset(path, tokenizer, transforms, labels, args.max_seq_length - args.num_image_embeds - 2)
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return dataset
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|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
# Required parameters
|
||||
parser.add_argument(
|
||||
"--data_dir",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="The input data dir. Should contain the .jsonl files for MMIMDB.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_type",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_name_or_path",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_dir",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="The output directory where the model predictions and checkpoints will be written.",
|
||||
)
|
||||
|
||||
# Other parameters
|
||||
parser.add_argument(
|
||||
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tokenizer_name",
|
||||
default="",
|
||||
type=str,
|
||||
help="Pretrained tokenizer name or path if not the same as model_name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cache_dir",
|
||||
default="",
|
||||
type=str,
|
||||
help="Where do you want to store the pre-trained models downloaded from s3",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_seq_length",
|
||||
default=128,
|
||||
type=int,
|
||||
help="The maximum total input sequence length after tokenization. Sequences longer "
|
||||
"than this will be truncated, sequences shorter will be padded.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num_image_embeds", default=1, type=int, help="Number of Image Embeddings from the Image Encoder"
|
||||
)
|
||||
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
|
||||
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
|
||||
parser.add_argument(
|
||||
"--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
|
||||
)
|
||||
|
||||
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
|
||||
parser.add_argument(
|
||||
"--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--gradient_accumulation_steps",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
||||
)
|
||||
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
|
||||
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
|
||||
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
|
||||
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
||||
parser.add_argument(
|
||||
"--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
|
||||
)
|
||||
parser.add_argument("--patience", default=5, type=int, help="Patience for Early Stopping.")
|
||||
parser.add_argument(
|
||||
"--max_steps",
|
||||
default=-1,
|
||||
type=int,
|
||||
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
|
||||
)
|
||||
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
|
||||
|
||||
parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
|
||||
parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
|
||||
parser.add_argument(
|
||||
"--eval_all_checkpoints",
|
||||
action="store_true",
|
||||
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
|
||||
)
|
||||
parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
|
||||
parser.add_argument("--num_workers", type=int, default=8, help="number of worker threads for dataloading")
|
||||
parser.add_argument(
|
||||
"--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
|
||||
)
|
||||
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
|
||||
|
||||
parser.add_argument(
|
||||
"--fp16",
|
||||
action="store_true",
|
||||
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--fp16_opt_level",
|
||||
type=str,
|
||||
default="O1",
|
||||
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
|
||||
"See details at https://nvidia.github.io/apex/amp.html",
|
||||
)
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
||||
parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
|
||||
parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
|
||||
args = parser.parse_args()
|
||||
|
||||
if (
|
||||
os.path.exists(args.output_dir)
|
||||
and os.listdir(args.output_dir)
|
||||
and args.do_train
|
||||
and not args.overwrite_output_dir
|
||||
):
|
||||
raise ValueError(
|
||||
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
|
||||
args.output_dir
|
||||
)
|
||||
)
|
||||
|
||||
# Setup distant debugging if needed
|
||||
if args.server_ip and args.server_port:
|
||||
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
|
||||
import ptvsd
|
||||
|
||||
print("Waiting for debugger attach")
|
||||
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
|
||||
ptvsd.wait_for_attach()
|
||||
|
||||
# Setup CUDA, GPU & distributed training
|
||||
if args.local_rank == -1 or args.no_cuda:
|
||||
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
||||
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
|
||||
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
||||
torch.cuda.set_device(args.local_rank)
|
||||
device = torch.device("cuda", args.local_rank)
|
||||
torch.distributed.init_process_group(backend="nccl")
|
||||
args.n_gpu = 1
|
||||
|
||||
args.device = device
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
|
||||
)
|
||||
logger.warning(
|
||||
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
|
||||
args.local_rank,
|
||||
device,
|
||||
args.n_gpu,
|
||||
bool(args.local_rank != -1),
|
||||
args.fp16,
|
||||
)
|
||||
|
||||
# Set seed
|
||||
set_seed(args)
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
if args.local_rank not in [-1, 0]:
|
||||
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
|
||||
|
||||
# Setup model
|
||||
labels = get_mmimdb_labels()
|
||||
num_labels = len(labels)
|
||||
args.model_type = args.model_type.lower()
|
||||
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
|
||||
transformer_config = config_class.from_pretrained(
|
||||
args.config_name if args.config_name else args.model_name_or_path
|
||||
)
|
||||
tokenizer = tokenizer_class.from_pretrained(
|
||||
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
|
||||
do_lower_case=args.do_lower_case,
|
||||
cache_dir=args.cache_dir if args.cache_dir else None,
|
||||
)
|
||||
transformer = model_class.from_pretrained(
|
||||
args.model_name_or_path, config=transformer_config, cache_dir=args.cache_dir if args.cache_dir else None
|
||||
)
|
||||
img_encoder = ImageEncoder(args)
|
||||
config = MMBTConfig(transformer_config, num_labels=num_labels)
|
||||
model = MMBTForClassification(config, transformer, img_encoder)
|
||||
|
||||
if args.local_rank == 0:
|
||||
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
|
||||
|
||||
model.to(args.device)
|
||||
|
||||
logger.info("Training/evaluation parameters %s", args)
|
||||
|
||||
# Training
|
||||
if args.do_train:
|
||||
train_dataset = load_examples(args, tokenizer, evaluate=False)
|
||||
label_frequences = train_dataset.get_label_frequencies()
|
||||
label_frequences = [label_frequences[l] for l in labels]
|
||||
label_weights = (
|
||||
torch.tensor(label_frequences, device=args.device, dtype=torch.float) / len(train_dataset)
|
||||
) ** -1
|
||||
criterion = nn.BCEWithLogitsLoss(pos_weight=label_weights)
|
||||
global_step, tr_loss = train(args, train_dataset, model, tokenizer, criterion)
|
||||
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
||||
|
||||
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
|
||||
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
||||
# Create output directory if needed
|
||||
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
|
||||
os.makedirs(args.output_dir)
|
||||
|
||||
logger.info("Saving model checkpoint to %s", args.output_dir)
|
||||
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
|
||||
# They can then be reloaded using `from_pretrained()`
|
||||
model_to_save = (
|
||||
model.module if hasattr(model, "module") else model
|
||||
) # Take care of distributed/parallel training
|
||||
torch.save(model_to_save.state_dict(), os.path.join(args.output_dir, WEIGHTS_NAME))
|
||||
tokenizer.save_pretrained(args.output_dir)
|
||||
|
||||
# Good practice: save your training arguments together with the trained model
|
||||
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
|
||||
|
||||
# Load a trained model and vocabulary that you have fine-tuned
|
||||
model = MMBTForClassification(config, transformer, img_encoder)
|
||||
model.load_state_dict(torch.load(os.path.join(args.output_dir, WEIGHTS_NAME)))
|
||||
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
|
||||
model.to(args.device)
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if args.do_eval and args.local_rank in [-1, 0]:
|
||||
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
||||
checkpoints = [args.output_dir]
|
||||
if args.eval_all_checkpoints:
|
||||
checkpoints = list(
|
||||
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
|
||||
)
|
||||
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
|
||||
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
||||
for checkpoint in checkpoints:
|
||||
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
||||
prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
|
||||
model = MMBTForClassification(config, transformer, img_encoder)
|
||||
model.load_state_dict(torch.load(checkpoint))
|
||||
model.to(args.device)
|
||||
result = evaluate(args, model, tokenizer, criterion, prefix=prefix)
|
||||
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
|
||||
results.update(result)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
143
examples/contrib/mm-imdb/utils_mmimdb.py
Normal file
143
examples/contrib/mm-imdb/utils_mmimdb.py
Normal file
@@ -0,0 +1,143 @@
|
||||
# coding=utf-8
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
# Copyright (c) HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
import os
|
||||
from collections import Counter
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
from PIL import Image
|
||||
from torch.utils.data import Dataset
|
||||
|
||||
|
||||
POOLING_BREAKDOWN = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
|
||||
|
||||
|
||||
class ImageEncoder(nn.Module):
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
model = torchvision.models.resnet152(pretrained=True)
|
||||
modules = list(model.children())[:-2]
|
||||
self.model = nn.Sequential(*modules)
|
||||
self.pool = nn.AdaptiveAvgPool2d(POOLING_BREAKDOWN[args.num_image_embeds])
|
||||
|
||||
def forward(self, x):
|
||||
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
|
||||
out = self.pool(self.model(x))
|
||||
out = torch.flatten(out, start_dim=2)
|
||||
out = out.transpose(1, 2).contiguous()
|
||||
return out # BxNx2048
|
||||
|
||||
|
||||
class JsonlDataset(Dataset):
|
||||
def __init__(self, data_path, tokenizer, transforms, labels, max_seq_length):
|
||||
self.data = [json.loads(l) for l in open(data_path)]
|
||||
self.data_dir = os.path.dirname(data_path)
|
||||
self.tokenizer = tokenizer
|
||||
self.labels = labels
|
||||
self.n_classes = len(labels)
|
||||
self.max_seq_length = max_seq_length
|
||||
|
||||
self.transforms = transforms
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data)
|
||||
|
||||
def __getitem__(self, index):
|
||||
sentence = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"], add_special_tokens=True))
|
||||
start_token, sentence, end_token = sentence[0], sentence[1:-1], sentence[-1]
|
||||
sentence = sentence[: self.max_seq_length]
|
||||
|
||||
label = torch.zeros(self.n_classes)
|
||||
label[[self.labels.index(tgt) for tgt in self.data[index]["label"]]] = 1
|
||||
|
||||
image = Image.open(os.path.join(self.data_dir, self.data[index]["img"])).convert("RGB")
|
||||
image = self.transforms(image)
|
||||
|
||||
return {
|
||||
"image_start_token": start_token,
|
||||
"image_end_token": end_token,
|
||||
"sentence": sentence,
|
||||
"image": image,
|
||||
"label": label,
|
||||
}
|
||||
|
||||
def get_label_frequencies(self):
|
||||
label_freqs = Counter()
|
||||
for row in self.data:
|
||||
label_freqs.update(row["label"])
|
||||
return label_freqs
|
||||
|
||||
|
||||
def collate_fn(batch):
|
||||
lens = [len(row["sentence"]) for row in batch]
|
||||
bsz, max_seq_len = len(batch), max(lens)
|
||||
|
||||
mask_tensor = torch.zeros(bsz, max_seq_len, dtype=torch.long)
|
||||
text_tensor = torch.zeros(bsz, max_seq_len, dtype=torch.long)
|
||||
|
||||
for i_batch, (input_row, length) in enumerate(zip(batch, lens)):
|
||||
text_tensor[i_batch, :length] = input_row["sentence"]
|
||||
mask_tensor[i_batch, :length] = 1
|
||||
|
||||
img_tensor = torch.stack([row["image"] for row in batch])
|
||||
tgt_tensor = torch.stack([row["label"] for row in batch])
|
||||
img_start_token = torch.stack([row["image_start_token"] for row in batch])
|
||||
img_end_token = torch.stack([row["image_end_token"] for row in batch])
|
||||
|
||||
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
|
||||
|
||||
|
||||
def get_mmimdb_labels():
|
||||
return [
|
||||
"Crime",
|
||||
"Drama",
|
||||
"Thriller",
|
||||
"Action",
|
||||
"Comedy",
|
||||
"Romance",
|
||||
"Documentary",
|
||||
"Short",
|
||||
"Mystery",
|
||||
"History",
|
||||
"Family",
|
||||
"Adventure",
|
||||
"Fantasy",
|
||||
"Sci-Fi",
|
||||
"Western",
|
||||
"Horror",
|
||||
"Sport",
|
||||
"War",
|
||||
"Music",
|
||||
"Musical",
|
||||
"Animation",
|
||||
"Biography",
|
||||
"Film-Noir",
|
||||
]
|
||||
|
||||
|
||||
def get_image_transforms():
|
||||
return transforms.Compose(
|
||||
[
|
||||
transforms.Resize(256),
|
||||
transforms.CenterCrop(224),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.46777044, 0.44531429, 0.40661017], std=[0.12221994, 0.12145835, 0.14380469],),
|
||||
]
|
||||
)
|
||||
Reference in New Issue
Block a user