def __init__( self, size=224 ):
global net
self.size = size
self.disable_grads( net )
self.checkpoint_path = "/home/vipul/Affine/Vision/classification/train/checkpoint"
self.checkpoint_name = "checkpoint.pth.tar"
load_checkpoint( net, os.path.join( self.checkpoint_path, self.checkpoint_name ) )
def main():
args = parse_args()
loader = load_val(config.val_path, args, distributed=False)
model = darknet().eval()
#model = torchvision.models.resnet101( pretrained=True ).eval()
checkpoint_path = os.path.join(config.checkpoint_path,
config.checkpoint_name)
load_checkpoint(model, checkpoint_path)
validate(loader, model, args)
def main():
model = load_checkpoint("model.pth")
testdata_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
images_list = []
random.shuffle(images_list)
index = 49
for i in os.listdir("new_images"):
if i != ".DS_Store":
images_list.append("new_images/" + i)
print(i)
for img in images_list:
infer_boundingbox(img, model, testdata_transform, index)
index += 1
# prepare test loader for the test set
test_file = args.data_path + args.test_file
test_data = ArticlesDataset(csv_file=test_file, vocab=vocab, label2id=label2id, max_text_len=args.text_len)
test_loader = DataLoader(test_data, batch_size=args.batch_size, shuffle=False)
scores_dict = {'f1': [], 'recall': [], 'precision': [], 'confidence': []}
for run_num in range(args.num_runs):
model_run_name = model_name + "_run"+str(run_num+1)
print("-"*10, "Run", run_num+1, "-"*10)
print("Model name:", model_run_name)
print("Loading model from", save_path + model_run_name + ".pt")
best_model = CNN(cnn_args=cnn_args, mlp_args=mlp_args).to(device)
optimizer = torch.optim.Adam(best_model.parameters(), lr=0.005)
load_checkpoint(save_path + model_run_name + ".pt", best_model, optimizer, device, log_file)
results = evaluate(best_model, test_loader)
scores_dict['f1'].append(results['f1'])
scores_dict['recall'].append(results['recall'])
scores_dict['precision'].append(results['precision'])
# if args.save_confidence is True:
# scores_dict['confidence'].append(results['confidence'])
# scores_dict['labels'].append(results['labels'])
# scores_dict['content'].append(results['content'])
# sentence_encodings = results['sentence_encodings']
scores_filename = save_path + model_name + "_test_scores.json"
scores_file = open(scores_filename, 'w')
precision = precision_score(y_true, y_pred, average='macro')
log(f"macro Recall: {recall}")
log(f"macro Precision: {precision}")
results = {'f1': macro_f1, 'recall': recall, 'precision': precision}
return results
if __name__ == "__main__":
#val_XT, val_yT = read_dataset("datafiles/val_enc.csv")
test_XT, test_yT = read_dataset("datafiles/test_enc.csv")
EMBEDDING_DIM = test_XT.shape[1]
OUTPUT_DIM = test_yT.shape[1]
BATCH_SIZE = 128
#model = FFNN(EMBEDDING_DIM, OUTPUT_DIM)
model = FFNN_DEEP(EMBEDDING_DIM, OUTPUT_DIM)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
test_custom_loader = CustomLoader(test_XT, test_yT)
test_loader = DataLoader(test_custom_loader,
batch_size=BATCH_SIZE,
shuffle=True)
load_checkpoint(f"datafiles/{MODEL_NAME}.pt", model, optimizer, DEVICE,
open(LOG_FILE, "a"))
results = evaluate(model, test_loader)
#log(results)