japanese-clip
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96 lines
4.0 KiB
96 lines
4.0 KiB
# coding=utf-8
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# Copyright 2022 rinna Co., Ltd.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from tqdm.auto import tqdm
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import numpy as np
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import torch
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def accuracy(output, target, topk=(1,)):
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output = torch.from_numpy(np.asarray(output))
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target = torch.from_numpy(np.asarray(target))
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pred = output.topk(max(topk), dim=1, largest=True, sorted=True)[1].t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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return [
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float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy())
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for k in topk
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]
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class ImagenetClassificationCallback:
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def __init__(
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self,
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imagenet_classes,
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imagenet_templates,
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imagenet_dataloader,
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):
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self.imagenet_classes = imagenet_classes
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self.imagenet_templates = imagenet_templates
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self.imagenet_dataloader = imagenet_dataloader
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def tokenize(self, tokenizer, examples, device):
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encoding_inputs = tokenizer(examples, max_length=76, padding="max_length", truncation=True, add_special_tokens=False)
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# add cls token at first place
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input_ids = [[tokenizer.cls_token_id] + ids for ids in encoding_inputs['input_ids']]
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attention_mask = [[1] + am for am in encoding_inputs['attention_mask']]
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position_ids = [list(range(0, len(input_ids[0])))] * len(examples)
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input_ids = torch.tensor(input_ids, dtype=torch.long, device=device)
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attention_mask = torch.tensor(attention_mask, dtype=torch.long, device=device)
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position_ids = torch.tensor(position_ids, dtype=torch.long, device=device)
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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"position_ids": position_ids,
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}
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def zeroshot_classifier(self, model, tokenizer, classnames, templates):
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zeroshot_weights = []
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for classname in tqdm(classnames):
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texts = [template.format(classname) for template in templates]
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class_embeddings = model.get_text_features(**self.tokenize(tokenizer, texts, model.device)).detach().cpu().numpy()
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class_embeddings = class_embeddings / np.linalg.norm(
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class_embeddings, axis=-1, keepdims=True
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)
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class_embedding = np.mean(class_embeddings, axis=0)
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class_embedding /= np.linalg.norm(class_embedding, axis=-1)
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zeroshot_weights.append(class_embedding)
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zeroshot_weights = np.stack(zeroshot_weights, axis=1)
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return zeroshot_weights
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def zeroshot(self, model, tokenizer) -> dict:
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print("Imagenet Zeroshot Classification...")
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zeroshot_weights = self.zeroshot_classifier(model, tokenizer, self.imagenet_classes, self.imagenet_templates)
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top_ns = [1, 5, 10, 100]
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acc_counters = [0.0 for _ in top_ns]
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n = 0.0
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for i, (images, target) in enumerate(tqdm(self.imagenet_dataloader)):
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target = target.numpy()
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# predict
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image_features = model.get_image_features(images.to(model.device)).detach().cpu().numpy()
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image_features = image_features / np.linalg.norm(image_features, axis=-1, keepdims=True)
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logits = 100.0 * image_features @ zeroshot_weights
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# measure accuracy
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accs = accuracy(logits, target, topk=top_ns)
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for j in range(len(top_ns)):
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acc_counters[j] += accs[j]
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n += images.shape[0]
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tops = {f"imagenet/top{top_ns[i]}": acc_counters[i] / n * 100 for i in range(len(top_ns))}
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return tops
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