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Fix tiny bugs #3

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Fix tiny bugs #3

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4 changes: 4 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -0,0 +1,4 @@
/*.pt
/*.json
/*.txt
*.pyc
17 changes: 10 additions & 7 deletions RET_CLIP/eval/zeroshot_evaluation.py
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Expand Up @@ -10,6 +10,8 @@
from tqdm import tqdm

import torch
import sys
sys.path.append(".")

from RET_CLIP.clip.model import convert_weights, CLIP
from RET_CLIP.clip import tokenize
Expand Down Expand Up @@ -109,8 +111,10 @@ def zero_shot_classifier(model, classnames, templates, args):
zeroshot_weights = []
for classname in tqdm(classnames):
texts = [_preprocess_text(template(classname)) for template in templates] # format with class
texts = tokenize(texts, context_length=args.context_length).to(args.gpu) # tokenize
class_embeddings = model(None, texts)
text_ids = tokenize(texts, context_length=args.context_length).to(args.gpu) # tokenize
# model(texts) returns a tuple of (text, text_left, text_right).
# Since we provide the input image as img_l, we choose text_left as the class embeddings.
class_embeddings = model(None, None, text_ids)[1]
class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
class_embedding = class_embeddings.mean(dim=0)
class_embedding /= class_embedding.norm()
Expand All @@ -136,7 +140,7 @@ def run(model, classifier, dataloader, args):
total_targets.append(target)

# predict
image_features = model(images, None)
image_features = model(images, None, None)
image_features /= image_features.norm(dim=-1, keepdim=True)
logits = (100.0 * image_features @ classifier).softmax(dim=-1)
total_logits.append(logits)
Expand Down Expand Up @@ -215,13 +219,12 @@ def run(model, classifier, dataloader, args):
# Map model to be loaded to specified single gpu.
loc = "cuda:{}".format(args.gpu)
checkpoint = torch.load(args.resume, map_location='cpu')
start_epoch = checkpoint["epoch"]
sd = checkpoint["state_dict"]
sd = checkpoint
if next(iter(sd.items()))[0].startswith('module'):
sd = {k[len('module.'):]: v for k, v in sd.items() if "bert.pooler" not in k}
model.load_state_dict(sd)
print(
f"=> loaded checkpoint '{args.resume}' (epoch {checkpoint['epoch']} @ {checkpoint['step']} steps)"
f"=> loaded checkpoint '{args.resume}'"
)

# Compute ensembled class embeddings
Expand Down Expand Up @@ -257,7 +260,7 @@ def json_prec_dump(data, prec=6):
json.loads(json.dumps(data), parse_float=lambda x: round(float(x), prec))
)

print(logits.size())
#print(logits.size())
output_dict = {
"model_name": "CN-CLIP-" + args.vision_model,
"dataset_name": args.dataset,
Expand Down
2 changes: 1 addition & 1 deletion RET_CLIP/training/train.py
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Expand Up @@ -15,7 +15,7 @@
import torch.nn.functional as F

from RET_CLIP.clip.model import convert_state_dict
from eval_RFMiD import eval_multiLabelCls_ViT, eval_multiLabelCls_RN50
from .eval_RFMiD import eval_multiLabelCls_ViT, eval_multiLabelCls_RN50


def is_master(args):
Expand Down