def convert_vit_checkpoint(model_name,
pytorch_dump_folder_path,
base_model=True):
"""
Copy/paste/tweak model's weights to our ViT structure.
"""
# define default ViT configuration
config = ViTConfig()
# patch_size
if model_name[-1] == "8":
config.patch_size = 8
# set labels if required
if not base_model:
config.num_labels = 1000
repo_id = "datasets/huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
config.hidden_size = 384
config.intermediate_size = 1536
config.num_hidden_layers = 12
config.num_attention_heads = 6
# load original model from torch hub
original_model = torch.hub.load("facebookresearch/dino:main", model_name)
original_model.eval()
# load state_dict of original model, remove and rename some keys
state_dict = original_model.state_dict()
if base_model:
remove_classification_head_(state_dict)
rename_keys = create_rename_keys(config, base_model=base_model)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config, base_model)
# load HuggingFace model
if base_model:
model = ViTModel(config, add_pooling_layer=False).eval()
else:
model = ViTForImageClassification(config).eval()
model.load_state_dict(state_dict)
# Check outputs on an image, prepared by ViTFeatureExtractor
feature_extractor = ViTFeatureExtractor()
encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
pixel_values = encoding["pixel_values"]
outputs = model(pixel_values)
if base_model:
final_hidden_state_cls_token = original_model(pixel_values)
assert torch.allclose(final_hidden_state_cls_token,
outputs.last_hidden_state[:, 0, :],
atol=1e-1)
else:
logits = original_model(pixel_values)
assert logits.shape == outputs.logits.shape
assert torch.allclose(logits, outputs.logits, atol=1e-3)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving feature extractor to {pytorch_dump_folder_path}")
feature_extractor.save_pretrained(pytorch_dump_folder_path)
def convert_vit_checkpoint(vit_name, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our ViT structure.
"""
# define default ViT configuration
config = ViTConfig()
base_model = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
base_model = True
config.patch_size = int(vit_name[-12:-10])
config.image_size = int(vit_name[-9:-6])
else:
config.num_labels = 1000
repo_id = "datasets/huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(
open(cached_download(hf_hub_url(repo_id, filename)), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
config.patch_size = int(vit_name[-6:-4])
config.image_size = int(vit_name[-3:])
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny"):
config.hidden_size = 192
config.intermediate_size = 768
config.num_hidden_layers = 12
config.num_attention_heads = 3
elif vit_name[9:].startswith("small"):
config.hidden_size = 384
config.intermediate_size = 1536
config.num_hidden_layers = 12
config.num_attention_heads = 6
else:
pass
else:
if vit_name[4:].startswith("small"):
config.hidden_size = 768
config.intermediate_size = 2304
config.num_hidden_layers = 8
config.num_attention_heads = 8
elif vit_name[4:].startswith("base"):
pass
elif vit_name[4:].startswith("large"):
config.hidden_size = 1024
config.intermediate_size = 4096
config.num_hidden_layers = 24
config.num_attention_heads = 16
elif vit_name[4:].startswith("huge"):
config.hidden_size = 1280
config.intermediate_size = 5120
config.num_hidden_layers = 32
config.num_attention_heads = 16
# load original model from timm
timm_model = timm.create_model(vit_name, pretrained=True)
timm_model.eval()
# load state_dict of original model, remove and rename some keys
state_dict = timm_model.state_dict()
if base_model:
remove_classification_head_(state_dict)
rename_keys = create_rename_keys(config, base_model)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config, base_model)
# load HuggingFace model
if vit_name[-5:] == "in21k":
model = ViTModel(config).eval()
else:
model = ViTForImageClassification(config).eval()
model.load_state_dict(state_dict)
# Check outputs on an image, prepared by ViTFeatureExtractor/DeiTFeatureExtractor
if "deit" in vit_name:
feature_extractor = DeiTFeatureExtractor(size=config.image_size)
else:
feature_extractor = ViTFeatureExtractor(size=config.image_size)
encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
pixel_values = encoding["pixel_values"]
outputs = model(pixel_values)
if base_model:
timm_pooled_output = timm_model.forward_features(pixel_values)
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(timm_pooled_output,
outputs.pooler_output,
atol=1e-3)
else:
timm_logits = timm_model(pixel_values)
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(timm_logits, outputs.logits, atol=1e-3)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving feature extractor to {pytorch_dump_folder_path}")
feature_extractor.save_pretrained(pytorch_dump_folder_path)
def convert_tr_ocr_checkpoint(checkpoint_url, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our VisionEncoderDecoderModel structure.
"""
# define encoder and decoder configs based on checkpoint_url
encoder_config = ViTConfig(image_size=384, qkv_bias=False)
decoder_config = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
decoder_config.encoder_hidden_size = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
encoder_config.hidden_size = 1024
encoder_config.intermediate_size = 4096
encoder_config.num_hidden_layers = 24
encoder_config.num_attention_heads = 16
decoder_config.encoder_hidden_size = 1024
else:
raise ValueError(
"Should either find 'base' or 'large' in checkpoint URL")
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
decoder_config.tie_word_embeddings = False
decoder_config.activation_function = "relu"
decoder_config.max_position_embeddings = 1024
decoder_config.scale_embedding = True
decoder_config.use_learned_position_embeddings = False
decoder_config.layernorm_embedding = False
# load HuggingFace model
encoder = ViTModel(encoder_config, add_pooling_layer=False)
decoder = TrOCRForCausalLM(decoder_config)
model = VisionEncoderDecoderModel(encoder=encoder, decoder=decoder)
model.eval()
# load state_dict of original model, rename some keys
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url,
map_location="cpu",
check_hash=True)["model"]
rename_keys = create_rename_keys(encoder_config, decoder_config)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, encoder_config)
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
val = state_dict.pop(key)
if key.startswith("decoder") and "output_projection" not in key:
state_dict["decoder.model." + key] = val
else:
state_dict[key] = val
# load state dict
model.load_state_dict(state_dict)
# Check outputs on an image
feature_extractor = ViTFeatureExtractor(size=encoder_config.image_size)
tokenizer = RobertaTokenizer.from_pretrained("roberta-large")
processor = TrOCRProcessor(feature_extractor, tokenizer)
pixel_values = processor(images=prepare_img(checkpoint_url),
return_tensors="pt").pixel_values
# verify logits
decoder_input_ids = torch.tensor(
[[model.config.decoder.decoder_start_token_id]])
outputs = model(pixel_values=pixel_values,
decoder_input_ids=decoder_input_ids)
logits = outputs.logits
expected_shape = torch.Size([1, 1, 50265])
if "trocr-base-handwritten" in checkpoint_url:
expected_slice = torch.tensor([
-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764,
1.7560, 8.7358, -1.5311
])
elif "trocr-large-handwritten" in checkpoint_url:
expected_slice = torch.tensor([
-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702,
5.6113, 2.0170
])
elif "trocr-base-printed" in checkpoint_url:
expected_slice = torch.tensor([
-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284,
-1.0232, -1.9661, -3.9210
])
elif "trocr-large-printed" in checkpoint_url:
expected_slice = torch.tensor([
-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466,
-0.3081, -0.8106, -1.7535
])
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(
logits[0, 0, :10], expected_slice,
atol=1e-3), "First elements of logits not as expected"
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving processor to {pytorch_dump_folder_path}")
processor.save_pretrained(pytorch_dump_folder_path)
