def main():
num_classes = 91
device = torch.device('cuda')
backbone = build_backbone()
transformer = Transformer(
d_model=256,
dropout=0.1,
nhead=8,
dim_feedforward=2048,
num_encoder_layers=6,
num_decoder_layers=6,
normalize_before=False,
return_intermediate_dec=True,
)
model = DETR(
backbone,
transformer,
num_classes=num_classes,
num_queries=100,
aux_loss=True,
)
checkpoint = torch.load('./detr-r50-e632da11.pth')
model.load_state_dict(checkpoint['model'])
model.to(device)
model.eval()
gen_wts(model, "detr")
def create_model(weights):
backbone = build_backbone()
transformer = Transformer(d_model=256, return_intermediate_dec=True)
model = DETR(backbone, transformer, num_classes=91, num_queries=100)
checkpoint = torch.load(weights, map_location='cpu')['model']
model.load_state_dict(checkpoint)
return model
aux_loss = True
backbone = build_backbone(lr_backbone, masks, backbone, dilation, hidden_dim, position_embedding)
transformer = build_transformer(hidden_dim, dropout, nheads, dim_feedforward, enc_layers, dec_layers, pre_norm)
model = DETR(
backbone,
transformer,
num_classes=num_classes,
num_queries=num_queries,
aux_loss=aux_loss,
)
transform = make_coco_transforms()
postprocessors = PostProcess()
checkpoint = torch.load('/home/palm/PycharmProjects/detr/snapshots/1/checkpoint00295.pth')
model.load_state_dict(checkpoint['model'])
train_ints, valid_ints, labels, max_box_per_image = create_csv_training_instances(
'/home/palm/PycharmProjects/algea/dataset/train_annotations',
'/home/palm/PycharmProjects/algea/dataset/test_annotations',
'/home/palm/PycharmProjects/algea/dataset/classes',
)
# os.listdir()
all_detections = []
all_annotations = []
model.cuda()
for instance in valid_ints:
t = time.time()
all_annotation = all_annotation_from_instance(instance)
target_image_ori = Image.open(instance["filename"])
target_image = transform(target_image_ori)