def check_encoder_decoder_model_from_pretrained_configs( self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs( config, decoder_config) self.assertTrue(encoder_decoder_config.decoder.is_decoder) enc_dec_model = VisionEncoderDecoderModel(encoder_decoder_config) enc_dec_model.to(torch_device) enc_dec_model.eval() self.assertTrue(enc_dec_model.config.is_encoder_decoder) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) self.assertEqual(outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size, )))
def check_encoder_decoder_model_generate(self, config, decoder_config, pixel_values=None, **kwargs): encoder_model, decoder_model = self.get_encoder_decoder_model( config, decoder_config) enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) # Generate until max length if hasattr(enc_dec_model.config, "eos_token_id"): enc_dec_model.config.eos_token_id = None if hasattr(enc_dec_model.config, "decoder") and hasattr( enc_dec_model.config.decoder, "eos_token_id"): enc_dec_model.config.decoder.eos_token_id = None enc_dec_model.to(torch_device) inputs = pixel_values # Bert does not have a bos token id, so use pad_token_id instead generated_output = enc_dec_model.generate( inputs, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id) self.assertEqual(generated_output.shape, (inputs.shape[0], ) + (decoder_config.max_length, ))
def check_save_and_load_encoder_decoder_model( self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) enc_dec_model.to(torch_device) enc_dec_model.eval() with torch.no_grad(): outputs = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) out_2 = outputs[0].cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname: enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname) enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname) VisionEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_pretrained_model_name_or_path=encoder_tmp_dirname, decoder_pretrained_model_name_or_path=decoder_tmp_dirname, ) after_outputs = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) out_1 = after_outputs[0].cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5)
def check_encoder_decoder_model( self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) self.assertTrue(enc_dec_model.config.decoder.is_decoder) self.assertTrue(enc_dec_model.config.decoder.add_cross_attention) self.assertTrue(enc_dec_model.config.is_encoder_decoder) enc_dec_model.to(torch_device) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_hidden_states=True, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) ) encoder_outputs = BaseModelOutput(last_hidden_state=outputs_encoder_decoder.encoder_hidden_states[-1]) outputs_encoder_decoder = enc_dec_model( encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) )
def check_encoder_decoder_model_output_attentions(self, config, attention_mask, decoder_config, decoder_input_ids, decoder_attention_mask, labels=None, pixel_values=None, **kwargs): # make the decoder inputs a different shape from the encoder inputs to harden the test decoder_input_ids = decoder_input_ids[:, :-1] decoder_attention_mask = decoder_attention_mask[:, :-1] encoder_model, decoder_model = self.get_encoder_decoder_model( config, decoder_config) enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) enc_dec_model.to(torch_device) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_attentions=True, ) encoder_attentions = outputs_encoder_decoder["encoder_attentions"] self.assertEqual(len(encoder_attentions), config.num_hidden_layers) # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens) image_size = to_2tuple(encoder_model.config.image_size) patch_size = to_2tuple(encoder_model.config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 2 self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len)) decoder_attentions = outputs_encoder_decoder["decoder_attentions"] num_decoder_layers = (decoder_config.num_decoder_layers if hasattr( decoder_config, "num_decoder_layers") else decoder_config.num_hidden_layers) self.assertEqual(len(decoder_attentions), num_decoder_layers) self.assertEqual( decoder_attentions[0].shape[-3:], (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]), ) cross_attentions = outputs_encoder_decoder["cross_attentions"] self.assertEqual(len(cross_attentions), num_decoder_layers) cross_attention_input_seq_len = decoder_input_ids.shape[-1] self.assertEqual( cross_attentions[0].shape[-3:], (decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len), )
def check_encoder_decoder_model_output_attentions(self, config, decoder_config, decoder_input_ids, decoder_attention_mask, labels=None, pixel_values=None, **kwargs): # make the decoder inputs a different shape from the encoder inputs to harden the test decoder_input_ids = decoder_input_ids[:, :-1] decoder_attention_mask = decoder_attention_mask[:, :-1] encoder_model, decoder_model = self.get_encoder_decoder_model( config, decoder_config) enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model) enc_dec_model.to(torch_device) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_attentions=True, ) encoder_attentions = outputs_encoder_decoder["encoder_attentions"] self.assertEqual(len(encoder_attentions), config.num_hidden_layers) # in Swin, the seq_len equals: seq_len = encoder_model.config.window_size**2 self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads[0], seq_len, seq_len)) decoder_attentions = outputs_encoder_decoder["decoder_attentions"] num_decoder_layers = (decoder_config.num_decoder_layers if hasattr( decoder_config, "num_decoder_layers") else decoder_config.num_hidden_layers) self.assertEqual(len(decoder_attentions), num_decoder_layers) self.assertEqual( decoder_attentions[0].shape[-3:], (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]), ) cross_attentions = outputs_encoder_decoder["cross_attentions"] self.assertEqual(len(cross_attentions), num_decoder_layers) encoder_seq_len = ((config.image_size // config.patch_size)** 2) // (4**(len(config.depths) - 1)) cross_attention_input_seq_len = decoder_input_ids.shape[-1] self.assertEqual( cross_attentions[0].shape[-3:], (decoder_config.num_attention_heads, cross_attention_input_seq_len, encoder_seq_len), )