| import sys | |
| import pandas | |
| import gradio | |
| import pathlib | |
| sys.path.append("lib") | |
| import torch | |
| from roberta2 import RobertaForSequenceClassification | |
| from transformers import AutoTokenizer | |
| from gradient_rollout import GradientRolloutExplainer | |
| from rollout import RolloutExplainer | |
| from integrated_gradients import IntegratedGradientsExplainer | |
| device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
| model = RobertaForSequenceClassification.from_pretrained("textattack/roberta-base-SST-2").to(device) | |
| tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-SST-2") | |
| ig_explainer = IntegratedGradientsExplainer(model, tokenizer) | |
| gr_explainer = GradientRolloutExplainer(model, tokenizer) | |
| ro_explainer = RolloutExplainer(model, tokenizer) | |
| def run(sent, gradient, rollout, ig, ig_baseline): | |
| a = gr_explainer(sent, gradient) | |
| b = ro_explainer(sent, rollout) | |
| c = ig_explainer(sent, ig, ig_baseline) | |
| return a, b, c | |
| examples = pandas.read_csv("examples.csv").to_numpy().tolist() | |
| with gradio.Blocks(title="Explanations with attention rollout") as iface: | |
| gradio.Markdown(pathlib.Path("description.md").read_text) | |
| with gradio.Row(equal_height=True): | |
| with gradio.Column(scale=4): | |
| sent = gradio.Textbox(label="Input sentence") | |
| with gradio.Column(scale=1): | |
| but = gradio.Button("Submit") | |
| with gradio.Row(equal_height=True): | |
| with gradio.Column(): | |
| rollout_layer = gradio.Slider( | |
| minimum=1, | |
| maximum=12, | |
| value=1, | |
| step=1, | |
| label="Select rollout start layer" | |
| ) | |
| with gradio.Column(): | |
| gradient_layer = gradio.Slider( | |
| minimum=1, | |
| maximum=12, | |
| value=8, | |
| step=1, | |
| label="Select gradient rollout start layer" | |
| ) | |
| with gradio.Column(): | |
| ig_layer = gradio.Slider( | |
| minimum=0, | |
| maximum=12, | |
| value=0, | |
| step=1, | |
| label="Select IG layer" | |
| ) | |
| ig_baseline = gradio.Dropdown( | |
| label="Baseline token", | |
| choices=['Unknown', 'Padding'], value="Unknown" | |
| ) | |
| with gradio.Row(equal_height=True): | |
| with gradio.Column(): | |
| gradio.Markdown("### Attention Rollout") | |
| rollout_result = gradio.HTML() | |
| with gradio.Column(): | |
| gradio.Markdown("### Gradient-weighted Attention Rollout") | |
| gradient_result = gradio.HTML() | |
| with gradio.Column(): | |
| gradio.Markdown("### Layer-Integrated Gradients") | |
| ig_result = gradio.HTML() | |
| gradio.Examples(examples, [sent]) | |
| with gradio.Accordion("Some more details"): | |
| gradio.Markdown(pathlib.Path("notice.md").read_text) | |
| gradient_layer.change(gr_explainer, [sent, gradient_layer], gradient_result) | |
| rollout_layer.change(ro_explainer, [sent, rollout_layer], rollout_result) | |
| ig_layer.change(ig_explainer, [sent, ig_layer, ig_baseline], ig_result) | |
| but.click(run, | |
| inputs=[sent, gradient_layer, rollout_layer, ig_layer, ig_baseline], | |
| outputs=[gradient_result, rollout_result, ig_result] | |
| ) | |
| iface.launch() | |