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add an out-of-the-box captioning and visualization demo
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,233 @@ | ||
| import gradio as gr | ||
| import torch | ||
| import copy | ||
| import time | ||
| import requests | ||
| import io | ||
| import numpy as np | ||
| import re | ||
|
|
||
| import ipdb | ||
|
|
||
| from PIL import Image | ||
|
|
||
| from vilt.config import ex | ||
| from vilt.modules import ViLTransformerSS | ||
|
|
||
| from vilt.modules.objectives import cost_matrix_cosine, ipot | ||
| from vilt.transforms import pixelbert_transform | ||
| from vilt.datamodules.datamodule_base import get_pretrained_tokenizer | ||
|
|
||
|
|
||
| @ex.automain | ||
| def main(_config): | ||
| _config = copy.deepcopy(_config) | ||
|
|
||
| loss_names = { | ||
| "itm": 0, | ||
| "mlm": 0.5, | ||
| "mpp": 0, | ||
| "vqa": 0, | ||
| "imgcls": 0, | ||
| "nlvr2": 0, | ||
| "irtr": 0, | ||
| "arc": 0, | ||
| } | ||
| tokenizer = get_pretrained_tokenizer(_config["tokenizer"]) | ||
|
|
||
| _config.update( | ||
| { | ||
| "loss_names": loss_names, | ||
| } | ||
| ) | ||
|
|
||
| model = ViLTransformerSS(_config) | ||
| model.setup("test") | ||
| model.eval() | ||
|
|
||
| device = "cuda:0" if _config["num_gpus"] > 0 else "cpu" | ||
| model.to(device) | ||
|
|
||
| def infer(url, mp_text, hidx): | ||
| try: | ||
| res = requests.get(url) | ||
| image = Image.open(io.BytesIO(res.content)).convert("RGB") | ||
| img = pixelbert_transform(size=384)(image) | ||
| img = img.unsqueeze(0).to(device) | ||
| except: | ||
| return False | ||
|
|
||
| batch = {"text": [""], "image": [None]} | ||
| tl = len(re.findall("\[MASK\]", mp_text)) | ||
| inferred_token = [mp_text] | ||
| batch["image"][0] = img | ||
|
|
||
| with torch.no_grad(): | ||
| for i in range(tl): | ||
| batch["text"] = inferred_token | ||
| encoded = tokenizer(inferred_token) | ||
| batch["text_ids"] = torch.tensor(encoded["input_ids"]).to(device) | ||
| batch["text_labels"] = torch.tensor(encoded["input_ids"]).to(device) | ||
| batch["text_masks"] = torch.tensor(encoded["attention_mask"]).to(device) | ||
| encoded = encoded["input_ids"][0][1:-1] | ||
| infer = model(batch) | ||
| mlm_logits = model.mlm_score(infer["text_feats"])[0, 1:-1] | ||
| mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1) | ||
| mlm_values[torch.tensor(encoded) != 103] = 0 | ||
| select = mlm_values.argmax().item() | ||
| encoded[select] = mlm_ids[select].item() | ||
| inferred_token = [tokenizer.decode(encoded)] | ||
|
|
||
| selected_token = "" | ||
| encoded = tokenizer(inferred_token) | ||
|
|
||
| if hidx > 0 and hidx < len(encoded["input_ids"][0][:-1]): | ||
| with torch.no_grad(): | ||
| batch["text"] = inferred_token | ||
| batch["text_ids"] = torch.tensor(encoded["input_ids"]).to(device) | ||
| batch["text_labels"] = torch.tensor(encoded["input_ids"]).to(device) | ||
| batch["text_masks"] = torch.tensor(encoded["attention_mask"]).to(device) | ||
| infer = model(batch) | ||
| txt_emb, img_emb = infer["text_feats"], infer["image_feats"] | ||
| txt_mask, img_mask = ( | ||
| infer["text_masks"].bool(), | ||
| infer["image_masks"].bool(), | ||
| ) | ||
| for i, _len in enumerate(txt_mask.sum(dim=1)): | ||
| txt_mask[i, _len - 1] = False | ||
| txt_mask[:, 0] = False | ||
| img_mask[:, 0] = False | ||
| txt_pad, img_pad = ~txt_mask, ~img_mask | ||
|
|
||
| cost = cost_matrix_cosine(txt_emb.float(), img_emb.float()) | ||
| joint_pad = txt_pad.unsqueeze(-1) | img_pad.unsqueeze(-2) | ||
| cost.masked_fill_(joint_pad, 0) | ||
|
|
||
| txt_len = (txt_pad.size(1) - txt_pad.sum(dim=1, keepdim=False)).to( | ||
| dtype=cost.dtype | ||
| ) | ||
| img_len = (img_pad.size(1) - img_pad.sum(dim=1, keepdim=False)).to( | ||
| dtype=cost.dtype | ||
| ) | ||
| T = ipot( | ||
| cost.detach(), | ||
| txt_len, | ||
| txt_pad, | ||
| img_len, | ||
| img_pad, | ||
| joint_pad, | ||
| 0.1, | ||
| 1000, | ||
| 1, | ||
| ) | ||
|
|
||
| plan = T[0] | ||
| plan_single = plan * len(txt_emb) | ||
| cost_ = plan_single.t() | ||
|
|
||
| cost_ = cost_[hidx][1:].cpu() | ||
|
|
||
| patch_index, (H, W) = infer["patch_index"] | ||
| heatmap = torch.zeros(H, W) | ||
| for i, pidx in enumerate(patch_index[0]): | ||
| h, w = pidx[0].item(), pidx[1].item() | ||
| heatmap[h, w] = cost_[i] | ||
|
|
||
| heatmap = (heatmap - heatmap.mean()) / heatmap.std() | ||
| heatmap = np.clip(heatmap, 1.0, 3.0) | ||
| heatmap = (heatmap - heatmap.min()) / (heatmap.max() - heatmap.min()) | ||
|
|
||
| _w, _h = image.size | ||
| overlay = Image.fromarray(np.uint8(heatmap * 255), "L").resize( | ||
| (_w, _h), resample=Image.NEAREST | ||
| ) | ||
| image_rgba = image.copy() | ||
| image_rgba.putalpha(overlay) | ||
| image = image_rgba | ||
|
|
||
| selected_token = tokenizer.convert_ids_to_tokens( | ||
| encoded["input_ids"][0][hidx] | ||
| ) | ||
|
|
||
| return [np.array(image), inferred_token[0], selected_token] | ||
|
|
||
| inputs = [ | ||
| gr.inputs.Textbox( | ||
| label="A url of an image.", | ||
| lines=5, | ||
| ), | ||
| gr.inputs.Textbox(label="A caption with [MASK] tokens to be filled.", lines=5), | ||
| gr.inputs.Slider( | ||
| minimum=0, | ||
| maximum=38, | ||
| step=1, | ||
| label="A index of token for heatmap visualization (ignored if zero)", | ||
| ), | ||
| ] | ||
| outputs = [ | ||
| gr.outputs.Image(label="Image"), | ||
| gr.outputs.Textbox(label="description"), | ||
| gr.outputs.Textbox(label="selected token"), | ||
| ] | ||
|
|
||
| interface = gr.Interface( | ||
| fn=infer, | ||
| inputs=inputs, | ||
| outputs=outputs, | ||
| server_name="0.0.0.0", | ||
| server_port=8888, | ||
| examples=[ | ||
| [ | ||
| "https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg", | ||
| "a display of flowers growing out and over the [MASK] [MASK] in front of [MASK] on a [MASK] [MASK].", | ||
| 0, | ||
| ], | ||
| [ | ||
| "https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg", | ||
| "a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.", | ||
| 4, | ||
| ], | ||
| [ | ||
| "https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg", | ||
| "a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.", | ||
| 11, | ||
| ], | ||
| [ | ||
| "https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg", | ||
| "a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.", | ||
| 15, | ||
| ], | ||
| [ | ||
| "https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg", | ||
| "a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.", | ||
| 18, | ||
| ], | ||
| [ | ||
| "https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg", | ||
| "a room with a [MASK], a [MASK], a [MASK], and a [MASK].", | ||
| 0, | ||
| ], | ||
| [ | ||
| "https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg", | ||
| "a room with a rug, a chair, a painting, and a plant.", | ||
| 5, | ||
| ], | ||
| [ | ||
| "https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg", | ||
| "a room with a rug, a chair, a painting, and a plant.", | ||
| 8, | ||
| ], | ||
| [ | ||
| "https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg", | ||
| "a room with a rug, a chair, a painting, and a plant.", | ||
| 11, | ||
| ], | ||
| [ | ||
| "https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg", | ||
| "a room with a rug, a chair, a painting, and a plant.", | ||
| 15, | ||
| ], | ||
| ], | ||
| ) | ||
|
|
||
| interface.launch(debug=True) |
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