remove generate.py

generate.py

@@ -1,74 +0,0 @@
-import sys, os
-
-current_path = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(current_path)
-
-# Main model -  ViTGPT2LM
-from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
-
-# Vit - as encoder
-from transformers import ViTFeatureExtractor
-from PIL import Image
-import requests
-import numpy as np
-
-# GPT2 / GPT2LM - as decoder
-from transformers import ViTFeatureExtractor, GPT2Tokenizer
-
-model_name_or_path = './outputs/ckpt_2/'
-flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(model_name_or_path)
-
-vit_model_name = 'google/vit-base-patch16-224-in21k'
-feature_extractor = ViTFeatureExtractor.from_pretrained(vit_model_name)
-
-gpt2_model_name = 'asi/gpt-fr-cased-small'
-tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
-
-max_length = 32
-num_beams = 16
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-
-
-# encoder data
-url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
-image = Image.open(requests.get(url, stream=True).raw)
-# batch dim is added automatically
-encoder_inputs = feature_extractor(images=image, return_tensors="jax")
-pixel_values = encoder_inputs.pixel_values
-print(f'pixel_values.shape = {pixel_values.shape}')
-
-# decoder data
-sentence = 'mon chien est mignon'
-# IMPORTANT: For training/evaluation/attention_mask/loss
-sentence += ' ' + tokenizer.eos_token
-# batch dim is added automatically
-decoder_inputs = tokenizer(sentence, return_tensors="jax")
-print(decoder_inputs)
-print(f'input_ids.shape = {decoder_inputs.input_ids.shape}')
-
-# model data
-inputs = dict(decoder_inputs)
-inputs['pixel_values'] = pixel_values
-
-
-logits = flax_vit_gpt2_lm(**inputs)[0]
-preds = np.argmax(logits, axis=-1)
-print('=' * 60)
-print('Flax: Vit-GPT2-LM')
-print('predicted token ids:')
-print(preds)
-print('=' * 60)
-
-
-# Generation!
-batch = {'pixel_values': pixel_values}
-generation = flax_vit_gpt2_lm.generate(batch['pixel_values'], **gen_kwargs)
-print('generation:')
-print(generation)
-print('=' * 60)
-
-token_ids = np.array(generation.sequences)[0]
-caption = tokenizer.decode(token_ids)
-print(f'token_ids: {token_ids}')
-print(f'caption: {caption}')
-print('=' * 60)