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