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test.py
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import os
import clip
import torch.nn as nn
from datasets import Action_DATASETS
from torch.utils.data import DataLoader
from tqdm import tqdm
# import wandb
import argparse
import shutil
from pathlib import Path
import yaml
from dotmap import DotMap
import pprint
import numpy
from modules.Visual_Prompt import visual_prompt
from utils.Augmentation import get_augmentation
import torch
from utils.Text_Prompt import *
import json
class TextCLIP(nn.Module):
def __init__(self, model):
super(TextCLIP, self).__init__()
self.model = model
def forward(self, text):
return self.model.encode_text(text)
class ImageCLIP(nn.Module):
def __init__(self, model):
super(ImageCLIP, self).__init__()
self.model = model
def forward(self, image):
return self.model.encode_image(image)
def ambiguity_split(config):
ambiguity_file = json.load(open(config.data.ambiguity_list, 'r'))
label_list = open(config.data.label_list, 'r')
category2label, label2category = {}, {}
for line in label_list.readlines()[1:]:
label, category = line.strip().split(',')
label = int(label)
category2label[category] = label
label2category[label] = category
ambiguity2label, label2ambiguity = {'easy':[], 'mid': [], 'hard': []}, {}
for mode in ambiguity_file.keys():
for category in ambiguity_file[mode]:
label = category2label[category]
label2ambiguity[label] = mode
ambiguity2label[mode].append(label)
return label2ambiguity, ambiguity2label
def validate(epoch, val_loader, classes, device, model, fusion_model, config, num_text_aug):
label2ambiguity, ambiguity2label = ambiguity_split(config)
print('Ambiguity split -> easy: {}, mid: {}, hard: {}.'.format(len(ambiguity2label['easy']), len(ambiguity2label['mid']), len(ambiguity2label['hard'])))
model.eval()
fusion_model.eval()
num = [0 for _ in range(len(label2ambiguity.keys()) + 1)]
corr_1 = [0 for _ in range(len(label2ambiguity.keys()) + 1)]
corr_5 = [0 for _ in range(len(label2ambiguity.keys()) + 1)]
with torch.no_grad():
text_inputs = classes.to(device)
text_features = model.encode_text(text_inputs)
for batch_idx, (image, class_id, _) in enumerate(tqdm(val_loader)):
image = image.view((-1, config.data.num_segments, 3) + image.size()[-2:])
b, t, c, h, w = image.size()
class_id = class_id.to(device)
image_input = image.to(device).view(-1, c, h, w)
image_features = model.encode_image(image_input).view(b, t, -1)
image_features = fusion_model(image_features)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = (100.0 * image_features @ text_features.T)
similarity = similarity.view(b, num_text_aug, -1).softmax(dim=-1)
similarity = similarity.mean(dim=1, keepdim=False)
values_1, indices_1 = similarity.topk(1, dim=-1)
values_5, indices_5 = similarity.topk(5, dim=-1)
num[0] += b
for i in range(b):
num[class_id[i]] +=1
if indices_1[i] == class_id[i]:
corr_1[0] += 1
corr_1[class_id[i]] += 1
if class_id[i] in indices_5[i]:
corr_5[0] += 1
corr_5[class_id[i]] += 1
top1 = [float(corr_1[i]) / num[i] * 100 if num[i] > 0 else -1 for i in range(len(num))]
top5 = [float(corr_5[i]) / num[i] * 100 if num[i] > 0 else -1 for i in range(len(num))]
valid_num, mean_top1, mean_top5 = 0, 0, 0
for idx in range(1, len(num)):
if num[idx] > 0:
valid_num += 1
mean_top1 += top1[idx]
mean_top5 += top5[idx]
mean_top1, mean_top5 = mean_top1 / valid_num, mean_top5 / valid_num
header = "| Category | Top1 | Top5 | Mean Top1 | Mean Top5 |"
divider = "+" + "-" * (len(header) - 2) + "+"
print(divider)
print(header)
print("| Overall | {0:.2f} | {1:.2f} | {2:.2f} | {3:.2f} |".format(top1[0], top5[0], mean_top1, mean_top5))
print(divider)
return top1[0]
def main():
global args, best_prec1
global global_step
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-cfg', default='')
parser.add_argument('--log_time', default='')
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f)
working_dir = os.path.join('./exp', config['network']['type'], config['network']['arch'], config['data']['dataset'],
args.log_time)
# wandb.init(project=config['network']['type'],
# name='{}_{}_{}_{}'.format(args.log_time, config['network']['type'], config['network']['arch'],
# config['data']['dataset']))
config = DotMap(config)
Path(working_dir).mkdir(parents=True, exist_ok=True)
shutil.copy(args.config, working_dir)
shutil.copy('test.py', working_dir)
device = "cuda" if torch.cuda.is_available() else "cpu" # If using GPU then use mixed precision training.
model, clip_state_dict = clip.load(config.network.arch, device=device, jit=False, tsm=config.network.tsm,
T=config.data.num_segments, dropout=config.network.drop_out,
emb_dropout=config.network.emb_dropout) # Must set jit=False for training ViT-B/32
transform_val = get_augmentation(False, config)
# ======================= old_version =======================
# fusion_model = visual_prompt(config.network.sim_header, clip_state_dict, config.data.num_segments)
# model_text = TextCLIP(model)
# model_image = ImageCLIP(model)
# model_text = torch.nn.DataParallel(model_text).cuda()
# model_image = torch.nn.DataParallel(model_image).cuda()
# fusion_model = torch.nn.DataParallel(fusion_model).cuda()
model_image = ImageCLIP(model).cuda()
model_text = TextCLIP(model).cuda()
fusion_model = visual_prompt(config.network.sim_header, clip_state_dict, config.data.num_segments).cuda()
# wandb.watch(model)
# wandb.watch(fusion_model)
val_data = Action_DATASETS(config.data.val_list, config.data.label_list, num_segments=config.data.num_segments,
image_tmpl=config.data.image_tmpl,
transform=transform_val, random_shift=config.random_shift)
val_loader = DataLoader(val_data, batch_size=config.data.batch_size, num_workers=config.data.workers, shuffle=False,
pin_memory=True, drop_last=True)
if device == "cpu":
model_text.float()
model_image.float()
else:
clip.model.convert_weights(
model_text) # Actually this line is unnecessary since clip by default already on float16
clip.model.convert_weights(model_image)
start_epoch = config.solver.start_epoch
if config.pretrain:
if os.path.isfile(config.pretrain):
print(("=> loading checkpoint '{}'".format(config.pretrain)))
checkpoint = torch.load(config.pretrain)
model.load_state_dict(checkpoint['model_state_dict'])
fusion_model.load_state_dict(checkpoint['fusion_model_state_dict'])
del checkpoint
else:
print(("=> no checkpoint found at '{}'".format(config.pretrain)))
classes, num_text_aug, text_dict = text_prompt(val_data)
best_prec1 = 0.0
prec1 = validate(start_epoch, val_loader, classes, device, model, fusion_model, config, num_text_aug)
if __name__ == '__main__':
main()