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import sys, os |
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current_path = os.path.dirname(os.path.abspath(__file__)) |
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sys.path.append(current_path) |
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from transformers import ViTFeatureExtractor |
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from PIL import Image |
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import requests |
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import numpy as np |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k') |
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encoder_inputs = feature_extractor(images=image, return_tensors="jax") |
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pixel_values = encoder_inputs.pixel_values |
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from transformers import ViTFeatureExtractor, GPT2Tokenizer |
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name = 'asi/gpt-fr-cased-small' |
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tokenizer = GPT2Tokenizer.from_pretrained(name) |
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decoder_inputs = tokenizer("mon chien est mignon", return_tensors="jax") |
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inputs = dict(decoder_inputs) |
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inputs['pixel_values'] = pixel_values |
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print(inputs) |
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from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration |
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flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained( |
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'.', |
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) |
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logits = flax_vit_gpt2_lm(**inputs)[0] |
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preds = np.argmax(logits, axis=-1) |
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print('=' * 60) |
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print('Flax: Vit + modified GPT2LM') |
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print(preds) |
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del flax_vit_gpt2_lm |
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