09wk-2: model의 입력파악, model의 사용연습

1. 강의영상

!pip uninstall mp2024pkg -y 
!pip install git+https://github.com/guebin/mp2024pkg.git

Found existing installation: mp2024pkg 1.0
Uninstalling mp2024pkg-1.0:
  Successfully uninstalled mp2024pkg-1.0
Collecting git+https://github.com/guebin/mp2024pkg.git
  Cloning https://github.com/guebin/mp2024pkg.git to /tmp/pip-req-build-178x45er
  Running command git clone --filter=blob:none --quiet https://github.com/guebin/mp2024pkg.git /tmp/pip-req-build-178x45er
  Resolved https://github.com/guebin/mp2024pkg.git to commit 2adb5fc04e589895edd76ec5da87aa37bf0dd70b
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: mp2024pkg
  Building wheel for mp2024pkg (setup.py) ... done
  Created wheel for mp2024pkg: filename=mp2024pkg-1.0-py3-none-any.whl size=4588 sha256=84356f47d6566dcda64e0d93070d9a1d9b6d9c9f962769858a4d823b589fe40b
  Stored in directory: /tmp/pip-ephem-wheel-cache-n0_tsfan/wheels/83/81/b1/63c03ca869bb1a35a588dce54e83ea32c6c21e6af0bbeaa640
Successfully built mp2024pkg
Installing collected packages: mp2024pkg
Successfully installed mp2024pkg-1.0

2. Imports

import transformers
import datasets
import huggingface_hub
import torch
import torchvision
import pytorchvideo.data
import PIL
import tarfile
import mp2024pkg as mp

/home/cgb3/anaconda3/envs/hf/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

3. Model 입력파악

- 아래중 하나의 방법순으로..

• 방법1: model.forward? 에서 시그니처를 확인
• 방법2: model.forward? 에서 사용예제를 확인
• 방법3: 인터넷을 활용한 외부 자료 확인 (공식문서, 공식튜토리얼, 신뢰할만한 블로그, ChatGPT등)
• 방법4: model.forward?? 를 보고 모든 코드를 뜯어봄 <— 하지마세요

- 모델의 입력이 어떤형태로 정리되어야 하는지 알아내는 확실한 방법은 없음

• 방법1,2,3 은 다른사람의 호의에 기대해야함.
• 방법4는 사실상 불가능

# 예제1 – 텍스트분류

model1 = transformers.AutoModelForSequenceClassification.from_pretrained(
    "distilbert/distilbert-base-uncased", num_labels=2
)

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

모델의 기본정보(config)

model1.config

DistilBertConfig {
  "_attn_implementation_autoset": true,
  "_name_or_path": "distilbert/distilbert-base-uncased",
  "activation": "gelu",
  "architectures": [
    "DistilBertForMaskedLM"
  ],
  "attention_dropout": 0.1,
  "dim": 768,
  "dropout": 0.1,
  "hidden_dim": 3072,
  "initializer_range": 0.02,
  "max_position_embeddings": 512,
  "model_type": "distilbert",
  "n_heads": 12,
  "n_layers": 6,
  "pad_token_id": 0,
  "qa_dropout": 0.1,
  "seq_classif_dropout": 0.2,
  "sinusoidal_pos_embds": false,
  "tie_weights_": true,
  "transformers_version": "4.46.2",
  "vocab_size": 30522
}

• max_position_embeddings: 512

모델의 입력파악

# model1.forward? 
# -- 결과가 너무 길어서 생략

사용예시1 – 입력나열, loss O

model1(
    input_ids = torch.tensor([[1,2,3,4], [2,3,4,5]]),
    labels = torch.tensor([0,0])
)

SequenceClassifierOutput(loss=tensor(0.7676, grad_fn=<NllLossBackward0>), logits=tensor([[-0.0565,  0.0915],
        [-0.0557,  0.0837]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시2 – **딕셔너리, loss O

model1_input = dict(
    input_ids = torch.tensor([[1,2,3,4], [2,3,4,5]]),
    labels = torch.tensor([0,0])
)
model1(**model1_input)

SequenceClassifierOutput(loss=tensor(0.7676, grad_fn=<NllLossBackward0>), logits=tensor([[-0.0565,  0.0915],
        [-0.0557,  0.0837]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시3 – 입력나열, loss X

model1(
    input_ids = torch.tensor([[1,2,3,4], [2,3,4,5]])
)

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0565,  0.0915],
        [-0.0557,  0.0837]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시4 – **딕셔너리, loss X

model1_input = dict(
    input_ids = torch.tensor([[1,2,3,4], [2,3,4,5]])
)
model1(**model1_input)

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0565,  0.0915],
        [-0.0557,  0.0837]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시5 – 초간단, loss X

model1(torch.tensor([[1,2,3,4], [2,3,4,5]]))

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0565,  0.0915],
        [-0.0557,  0.0837]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시1~5에서 model1() 대신에 model1.forward()를 사용해도 된다.

#

# 예제2 – 이미지분류

model2 = transformers.AutoModelForImageClassification.from_pretrained(
    "google/vit-base-patch16-224-in21k",
    num_labels=3 # 그냥 대충 3이라고 했음.. 별 이유는 없음
)

Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

모델의 기본정보(config)

model2.config

ViTConfig {
  "_attn_implementation_autoset": true,
  "_name_or_path": "google/vit-base-patch16-224-in21k",
  "architectures": [
    "ViTModel"
  ],
  "attention_probs_dropout_prob": 0.0,
  "encoder_stride": 16,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.0,
  "hidden_size": 768,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1",
    "2": "LABEL_2"
  },
  "image_size": 224,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1,
    "LABEL_2": 2
  },
  "layer_norm_eps": 1e-12,
  "model_type": "vit",
  "num_attention_heads": 12,
  "num_channels": 3,
  "num_hidden_layers": 12,
  "patch_size": 16,
  "qkv_bias": true,
  "transformers_version": "4.46.2"
}

• image_size: 224

# mp.tab(model2)
# -- 결과가 너무 길어서 생략 

model2.config.num_channels

3

모델의 입력파악

# model2.forward?
# -- 결과가 너무 길어서 생략

torch.randn(2,3,64,64)

tensor([[[[ 0.3649, -1.2605,  0.2920,  ..., -0.9787, -1.4717,  0.4677],
          [ 0.4702, -0.2664,  2.0551,  ..., -1.4783,  2.6271, -0.7880],
          [ 1.0891,  0.1022,  0.0211,  ...,  0.5663,  0.9881, -0.2273],
          ...,
          [ 0.2803,  1.3142,  0.4706,  ..., -1.1566,  0.1984,  1.4354],
          [ 0.2090,  0.6016,  0.5450,  ..., -0.1247,  0.8066, -0.2222],
          [ 1.1755,  1.1512,  0.7503,  ..., -1.2212,  0.2055,  0.3130]],

         [[ 0.0515, -0.7000, -1.4944,  ...,  0.5087,  0.7801, -0.0497],
          [-0.6095,  0.6269, -0.2168,  ..., -1.0991, -1.9875,  0.6154],
          [ 0.3600,  0.1016, -2.0896,  ..., -2.1648, -0.7579, -0.0747],
          ...,
          [-1.4642, -1.3562, -0.9592,  ..., -0.8820,  2.6860, -1.0333],
          [-1.6126,  0.2651,  0.3171,  ...,  0.4368, -0.2348, -0.5822],
          [ 0.6224,  0.8832,  0.4589,  ..., -1.3447,  0.5074, -1.2563]],

         [[-0.1654,  0.4390, -0.3119,  ..., -0.7779,  1.6596, -1.8286],
          [ 0.6203, -0.0072,  0.5610,  ...,  1.1161,  1.4232, -1.1180],
          [-0.3927,  2.7406,  0.6024,  ...,  0.2374,  0.8350, -1.3866],
          ...,
          [-1.0612,  0.6898,  0.5559,  ..., -1.7000, -0.3696, -0.7335],
          [-0.3483,  1.0322, -0.3756,  ..., -0.6278, -1.5500,  1.3041],
          [ 0.3224,  1.6797,  0.8219,  ...,  0.7146, -0.0869,  0.9379]]],


        [[[ 0.0124, -0.9813,  0.4385,  ...,  0.0203,  0.1493,  0.0797],
          [ 0.6513,  0.8768, -0.3780,  ...,  0.9154, -0.1878,  1.2205],
          [ 0.1238, -0.1669,  0.1737,  ...,  0.8929,  0.4685,  0.4024],
          ...,
          [-0.4388,  0.1257, -0.3490,  ..., -1.1260, -1.2975, -0.8466],
          [-0.5190, -0.1010,  1.6575,  ...,  0.7269, -1.4265, -1.0750],
          [ 1.9482, -0.7578, -0.3996,  ..., -0.2626, -1.3259,  0.5262]],

         [[ 1.9872, -1.0255,  0.1767,  ...,  0.6125, -0.5061,  0.8420],
          [ 0.7715, -0.5411, -1.4627,  ..., -2.6088, -0.5574,  0.5872],
          [ 1.3500, -0.6974, -1.0678,  ...,  0.3034, -0.1847, -0.5619],
          ...,
          [ 1.4522, -0.4482, -0.8592,  ...,  0.4603,  0.6203, -0.2643],
          [-0.1335,  1.5470, -0.4339,  ..., -1.5276, -0.9974,  0.1557],
          [ 0.4852, -0.0337, -0.1306,  ..., -2.4541, -1.3687, -0.6767]],

         [[-0.9349,  1.5542, -0.8323,  ...,  0.6367,  0.8844,  1.3362],
          [-0.5194, -1.8781, -0.4988,  ..., -1.5334,  0.5765,  0.0722],
          [-1.5885, -2.2619,  0.5158,  ...,  0.6815, -0.0635, -0.0362],
          ...,
          [ 0.5923,  0.4383, -1.0135,  ..., -0.3842,  0.3017, -2.5947],
          [-0.7695,  0.5169, -0.4380,  ...,  3.1244, -0.3718,  0.8871],
          [-1.9921,  0.0219, -0.1598,  ..., -0.2551,  0.3711,  0.4163]]]])

사용예시1 – 입력나열, loss O

torch.random.manual_seed(42)
model2(
    pixel_values = torch.randn(2,3,224,224),
    labels = torch.tensor([0,1])
)

ImageClassifierOutput(loss=tensor(1.1627, grad_fn=<NllLossBackward0>), logits=tensor([[-0.1805,  0.0750,  0.0782],
        [-0.1494,  0.0548,  0.0913]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시2 – **딕셔너리, loss O

torch.random.manual_seed(42)
model2_input = dict(
    pixel_values = torch.randn(2,3,224,224),
    labels = torch.tensor([0,1])
)
model2(**model2_input)

ImageClassifierOutput(loss=tensor(1.1627, grad_fn=<NllLossBackward0>), logits=tensor([[-0.1805,  0.0750,  0.0782],
        [-0.1494,  0.0548,  0.0913]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시3 – 입력나열, loss X

torch.random.manual_seed(42)
model2(
    pixel_values = torch.randn(2,3,224,224)
)

ImageClassifierOutput(loss=None, logits=tensor([[-0.1805,  0.0750,  0.0782],
        [-0.1494,  0.0548,  0.0913]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시4 – **딕셔너리, loss X

torch.random.manual_seed(42)
model2_input = dict(
    pixel_values = torch.randn(2,3,224,224),
)
model2(**model2_input)

ImageClassifierOutput(loss=None, logits=tensor([[-0.1805,  0.0750,  0.0782],
        [-0.1494,  0.0548,  0.0913]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시5 – 초간단, loss X

torch.random.manual_seed(42)
model2(torch.randn(2,3,224,224))

ImageClassifierOutput(loss=None, logits=tensor([[-0.1805,  0.0750,  0.0782],
        [-0.1494,  0.0548,  0.0913]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

# 예제3 – 동영상분류

model3 = transformers.VideoMAEForVideoClassification.from_pretrained(
    "MCG-NJU/videomae-base",
)

Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

모델의 기본정보(config)

model3.config

VideoMAEConfig {
  "_attn_implementation_autoset": true,
  "_name_or_path": "MCG-NJU/videomae-base",
  "architectures": [
    "VideoMAEForPreTraining"
  ],
  "attention_probs_dropout_prob": 0.0,
  "decoder_hidden_size": 384,
  "decoder_intermediate_size": 1536,
  "decoder_num_attention_heads": 6,
  "decoder_num_hidden_layers": 4,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.0,
  "hidden_size": 768,
  "image_size": 224,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "model_type": "videomae",
  "norm_pix_loss": true,
  "num_attention_heads": 12,
  "num_channels": 3,
  "num_frames": 16,
  "num_hidden_layers": 12,
  "patch_size": 16,
  "qkv_bias": true,
  "torch_dtype": "float32",
  "transformers_version": "4.46.2",
  "tubelet_size": 2,
  "use_mean_pooling": false
}

• image_size: 224
• num_frames: 16

모델의 입력파악

# model3.forward?
# -- 결과가 너무 길어서 생략

사용예시1 – 입력나열, loss O

torch.random.manual_seed(42)
model3(
    pixel_values = torch.randn(4,16,3,224,224),
    labels = torch.tensor([0,1,0,1])
)

ImageClassifierOutput(loss=tensor(0.7254, grad_fn=<NllLossBackward0>), logits=tensor([[-0.7084, -0.2919],
        [-0.5846, -0.1854],
        [-0.6635, -0.2402],
        [-0.7193, -0.3811]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시2 – **딕셔너리, loss O

torch.random.manual_seed(42)
model3_input = dict(
    pixel_values = torch.randn(4,16,3,224,224),
    labels = torch.tensor([0,1,0,1])
)
model3(**model3_input)

ImageClassifierOutput(loss=tensor(0.7254, grad_fn=<NllLossBackward0>), logits=tensor([[-0.7084, -0.2919],
        [-0.5846, -0.1854],
        [-0.6635, -0.2402],
        [-0.7193, -0.3811]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시3 – 입력나열, loss X

torch.random.manual_seed(42)
model3(
    pixel_values = torch.randn(4,16,3,224,224)
)

ImageClassifierOutput(loss=None, logits=tensor([[-0.7084, -0.2919],
        [-0.5846, -0.1854],
        [-0.6635, -0.2402],
        [-0.7193, -0.3811]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시4 – **딕셔너리, loss X

torch.random.manual_seed(42)
model3_input = dict(
    pixel_values = torch.randn(4,16,3,224,224),
)
model3(**model3_input)

ImageClassifierOutput(loss=None, logits=tensor([[-0.7084, -0.2919],
        [-0.5846, -0.1854],
        [-0.6635, -0.2402],
        [-0.7193, -0.3811]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

사용예시5 – 초간단, loss X

torch.random.manual_seed(42)
model3(torch.randn(4,16,3,224,224))

ImageClassifierOutput(loss=None, logits=tensor([[-0.7084, -0.2919],
        [-0.5846, -0.1854],
        [-0.6635, -0.2402],
        [-0.7193, -0.3811]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

4. Model 사용 연습

A. 텍스트

model1 = transformers.AutoModelForSequenceClassification.from_pretrained(
    "distilbert/distilbert-base-uncased", num_labels=2
)
tokenizer = transformers.AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# 예제1 – imdb

imdb = datasets.load_dataset('imdb')

d = imdb['train'].select(range(3))
d

Dataset({
    features: ['text', 'label'],
    num_rows: 3
})

(풀이1)

실패

model1.forward(torch.tensor(tokenizer(d['text'])['input_ids']))

ValueError: expected sequence of length 363 at dim 1 (got 304)

원인분석

mp.show_list(
    tokenizer(d['text'])['input_ids']
)

List Overview:
Total items: 3

1. list[0]
   - Type: list
   - Length: 363
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mp.show_list(
    tokenizer(d['text'], padding=True)['input_ids']
)

List Overview:
Total items: 3

1. list[0]
   - Type: list
   - Length: 363
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2. list[1]
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   - Length: 363
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성공

model1(torch.tensor(tokenizer(d['text'], padding=True)['input_ids']))

We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0174,  0.1774],
        [-0.0269,  0.2127],
        [-0.0322,  0.2528]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

(풀이2)

model1(tokenizer(d['text'], padding=True, return_tensors="pt")['input_ids'])

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0174,  0.1774],
        [-0.0269,  0.2127],
        [-0.0322,  0.2528]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

# 예제2 – emotion

emotion = datasets.load_dataset('emotion')

d = emotion['train'].select(range(3))
d

Dataset({
    features: ['text', 'label'],
    num_rows: 3
})

(풀이)

model1(torch.tensor(tokenizer(d['text'],padding=True)['input_ids']))

SequenceClassifierOutput(loss=None, logits=tensor([[0.0163, 0.2285],
        [0.0100, 0.2066],
        [0.0087, 0.2619]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

# 예제3 – MBTI

# mbti1.csv 파일 다운로드 
!wget https://raw.githubusercontent.com/guebin/MP2024/refs/heads/main/posts/mbti_1.csv

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
    - Avoid using `tokenizers` before the fork if possible
    - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

--2024-11-15 19:18:39--  https://raw.githubusercontent.com/guebin/MP2024/refs/heads/main/posts/mbti_1.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 62856486 (60M) [text/plain]
Saving to: ‘mbti_1.csv.1’

mbti_1.csv.1        100%[===================>]  59.94M   107MB/s    in 0.6s    

2024-11-15 19:18:44 (107 MB/s) - ‘mbti_1.csv.1’ saved [62856486/62856486]

d = datasets.Dataset.from_csv("mbti_1.csv").select(range(3))
d

Generating train split: 8675 examples [00:00, 16512.48 examples/s]

Dataset({
    features: ['type', 'posts'],
    num_rows: 3
})

(풀이1)

실패

model1(torch.tensor(tokenizer(d['posts'],padding=True)['input_ids']))

Token indices sequence length is longer than the specified maximum sequence length for this model (2102 > 512). Running this sequence through the model will result in indexing errors

RuntimeError: The size of tensor a (2102) must match the size of tensor b (512) at non-singleton dimension 1

원인분석

mp.show_list(
    tokenizer(d['posts'],padding=True)['input_ids']
)

List Overview:
Total items: 3

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mp.show_list(
    tokenizer(d['posts'],truncation=True)['input_ids']
)

List Overview:
Total items: 3

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성공

model1(torch.tensor(tokenizer(d['posts'],truncation=True)['input_ids']))
#model1(tokenizer(d['posts'],truncation=True,return_tensors="pt")['input_ids'])

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0450,  0.1728],
        [-0.0259,  0.1809],
        [-0.0611,  0.1994]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

(풀이2) –모델설정변경 (퀴즈5, 모델의 프레임수를 4로 바꾸는 예제에서 사용한 테크닉)

distilbert/distilbert-base-uncased 설정값 부르기

config = transformers.AutoConfig.from_pretrained(
    "distilbert/distilbert-base-uncased"
)
config

DistilBertConfig {
  "_name_or_path": "distilbert/distilbert-base-uncased",
  "activation": "gelu",
  "architectures": [
    "DistilBertForMaskedLM"
  ],
  "attention_dropout": 0.1,
  "dim": 768,
  "dropout": 0.1,
  "hidden_dim": 3072,
  "initializer_range": 0.02,
  "max_position_embeddings": 512,
  "model_type": "distilbert",
  "n_heads": 12,
  "n_layers": 6,
  "pad_token_id": 0,
  "qa_dropout": 0.1,
  "seq_classif_dropout": 0.2,
  "sinusoidal_pos_embds": false,
  "tie_weights_": true,
  "transformers_version": "4.46.2",
  "vocab_size": 30522
}

설정값변경

config.max_position_embeddings = 2200

설정값으로 모델불러오기

model1_large = transformers.AutoModelForSequenceClassification.from_config(
    config=config
)

모델사용

model1_large(torch.tensor(tokenizer(d['posts'],padding=True)['input_ids']))

SequenceClassifierOutput(loss=None, logits=tensor([[ 0.1023,  0.1124],
        [ 0.1443, -0.0719],
        [ 0.2653, -0.0368]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

model1_large(**tokenizer(d['posts'],padding=True,return_tensors="pt"))

SequenceClassifierOutput(loss=None, logits=tensor([[0.1730, 0.0042],
        [0.2185, 0.0896],
        [0.3406, 0.0155]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

# 예제4 – sms_spam

sms_spam = datasets.load_dataset('sms_spam')['train'].train_test_split(test_size=0.2, seed=42)
sms_spam

DatasetDict({
    train: Dataset({
        features: ['sms', 'label'],
        num_rows: 4459
    })
    test: Dataset({
        features: ['sms', 'label'],
        num_rows: 1115
    })
})

d = sms_spam['train'].select(range(3))
d

Dataset({
    features: ['sms', 'label'],
    num_rows: 3
})

(풀이)

model1(**tokenizer(d['sms'],padding=True,return_tensors="pt"))

SequenceClassifierOutput(loss=None, logits=tensor([[-0.0118,  0.1699],
        [ 0.0273,  0.1815],
        [-0.0208,  0.2202]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

B. 이미지

model2 = transformers.AutoModelForImageClassification.from_pretrained(
    "google/vit-base-patch16-224-in21k",
    num_labels=3 # 그냥 대충 3이라고 했음.. 별 이유는 없음
)

Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# 예제1 – food101

d = datasets.load_dataset("food101", split="train[:4]")
d

Dataset({
    features: ['image', 'label'],
    num_rows: 4
})

(예비학습) – torchvision.transforms 에서 제공하는 기능들은 배치처리가 가능한가?

to_tensor = torchvision.transforms.ToTensor()

to_tensor(d['image'][0])

tensor([[[0.1216, 0.1137, 0.1098,  ..., 0.0039, 0.0039, 0.0000],
         [0.1255, 0.1216, 0.1176,  ..., 0.0039, 0.0039, 0.0000],
         [0.1294, 0.1255, 0.1255,  ..., 0.0039, 0.0000, 0.0000],
         ...,
         [0.2588, 0.2745, 0.2863,  ..., 0.3765, 0.3882, 0.3922],
         [0.2353, 0.2471, 0.2667,  ..., 0.3373, 0.3373, 0.3373],
         [0.2235, 0.2275, 0.2471,  ..., 0.3333, 0.3176, 0.3059]],

        [[0.1373, 0.1294, 0.1255,  ..., 0.1020, 0.1020, 0.0980],
         [0.1412, 0.1373, 0.1333,  ..., 0.1020, 0.1020, 0.0980],
         [0.1451, 0.1412, 0.1412,  ..., 0.1020, 0.0980, 0.0980],
         ...,
         [0.2471, 0.2627, 0.2745,  ..., 0.3647, 0.3765, 0.3882],
         [0.2235, 0.2353, 0.2549,  ..., 0.3255, 0.3333, 0.3333],
         [0.2118, 0.2157, 0.2353,  ..., 0.3216, 0.3137, 0.3020]],

        [[0.1412, 0.1333, 0.1294,  ..., 0.0902, 0.0902, 0.0863],
         [0.1451, 0.1412, 0.1451,  ..., 0.0902, 0.0902, 0.0863],
         [0.1490, 0.1451, 0.1529,  ..., 0.0902, 0.0863, 0.0863],
         ...,
         [0.1725, 0.1882, 0.2000,  ..., 0.2431, 0.2549, 0.2667],
         [0.1490, 0.1608, 0.1804,  ..., 0.2039, 0.2118, 0.2118],
         [0.1373, 0.1412, 0.1608,  ..., 0.2000, 0.1922, 0.1804]]])

to_tensor(d['image'])

TypeError: pic should be PIL Image or ndarray. Got <class 'list'>

(풀이)

compose = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Resize((224,224))
])

torch.stack(list(map(compose,d['image'])),axis=0).shape

torch.Size([4, 3, 224, 224])

model2.forward(
    torch.stack(list(map(compose,d['image'])),axis=0)
)

ImageClassifierOutput(loss=None, logits=tensor([[ 0.0472,  0.0317, -0.1932],
        [ 0.2093,  0.1209, -0.0129],
        [ 0.0555,  0.0957, -0.0811],
        [-0.0682,  0.0363,  0.0317]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

# 예제2

beans = datasets.load_dataset('beans')
d = beans['train'].select(range(4))
d

Dataset({
    features: ['image_file_path', 'image', 'labels'],
    num_rows: 4
})

(풀이)

model2(torch.stack(list(map(compose,d['image'])),axis=0))

ImageClassifierOutput(loss=None, logits=tensor([[ 0.0545, -0.0993, -0.0386],
        [ 0.0576, -0.1576, -0.0019],
        [ 0.0982, -0.1531, -0.1147],
        [ 0.0570, -0.0575, -0.1455]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)

#

C. 동영상

model3 = transformers.VideoMAEForVideoClassification.from_pretrained(
    "MCG-NJU/videomae-base",
)

Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# 예제1 – UCF101_subset

file_path = huggingface_hub.hf_hub_download(
    repo_id="sayakpaul/ucf101-subset",
    filename="UCF101_subset.tar.gz",
    repo_type="dataset"
)
# file_path는 다운로드한 압축파일이 존재하는 경로와 파일명이 string으로 저장되어있음.
with tarfile.open(file_path) as t:
     t.extractall("./data") # 여기에서 "."은 현재폴더라는 의미

mp.tree("./data")

└── UCF101_subset
    ├── test
    │   ├── ApplyEyeMakeup
    │   │   ├── UCF101
    │   │   ├── v_ApplyEyeMakeup_g03_c01.avi
    │   │   └── ...
    │   │   └── v_ApplyEyeMakeup_g23_c06.avi
    │   ├── ApplyLipstick
    │   │   ├── UCF101
    │   │   ├── v_ApplyLipstick_g14_c01.avi
    │   │   └── ...
    │   │   └── v_ApplyLipstick_g16_c04.avi
    │   └── ...
    │   └── BenchPress
    │       ├── UCF101
    │       ├── v_BenchPress_g05_c02.avi
    │       └── ...
    │       └── v_BenchPress_g25_c06.avi
    ├── train
    │   ├── ApplyEyeMakeup
    │   │   ├── UCF101
    │   │   ├── v_ApplyEyeMakeup_g02_c03.avi
    │   │   └── ...
    │   │   └── v_ApplyEyeMakeup_g25_c07.avi
    │   ├── ApplyLipstick
    │   │   ├── UCF101
    │   │   ├── v_ApplyLipstick_g01_c02.avi
    │   │   └── ...
    │   │   └── v_ApplyLipstick_g24_c05.avi
    │   └── ...
    │   └── BenchPress
    │       ├── UCF101
    │       ├── v_BenchPress_g01_c05.avi
    │       └── ...
    │       └── v_BenchPress_g24_c05.avi
    └── val
        ├── ApplyEyeMakeup
        │   ├── UCF101
        │   ├── v_ApplyEyeMakeup_g01_c01.avi
        │   ├── v_ApplyEyeMakeup_g14_c05.avi
        │   └── v_ApplyEyeMakeup_g20_c04.avi
        ├── ApplyLipstick
        │   ├── UCF101
        │   ├── v_ApplyLipstick_g10_c04.avi
        │   ├── v_ApplyLipstick_g20_c04.avi
        │   └── v_ApplyLipstick_g25_c02.avi
        └── ...
        └── BenchPress
            ├── UCF101
            ├── v_BenchPress_g11_c05.avi
            ├── v_BenchPress_g17_c02.avi
            └── v_BenchPress_g17_c06.avi

video_path = "./data/UCF101_subset/test/BenchPress/v_BenchPress_g05_c02.avi"
video = pytorchvideo.data.encoded_video.EncodedVideo.from_path(video_path).get_clip(0, float('inf'))['video']
video.shape

torch.Size([3, 67, 240, 320])

(풀이)

model3(
    #video.permute(1,0,2,3)[:16,:,:224,:224].unsqueeze(0)
    #video.permute(1,0,2,3)[:16,:,:224,:224].reshape(1,16,3,224,224)
    torch.stack([video.permute(1,0,2,3)[:16,:,:224,:224]])
)

ImageClassifierOutput(loss=None, logits=tensor([[-0.1375,  0.4270]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)