import argparse, os, sys, glob, math, time import torch import numpy as np from omegaconf import OmegaConf import streamlit as st from streamlit import caching from PIL import Image from main import instantiate_from_config, DataModuleFromConfig from torch.utils.data import DataLoader from torch.utils.data.dataloader import default_collate rescale = lambda x: (x + 1.) / 2. def bchw_to_st(x): return rescale(x.detach().cpu().numpy().transpose(0,2,3,1)) def save_img(xstart, fname): I = (xstart.clip(0,1)[0]*255).astype(np.uint8) Image.fromarray(I).save(fname) def get_interactive_image(resize=False): image = st.file_uploader("Input", type=["jpg", "JPEG", "png"]) if image is not None: image = Image.open(image) if not image.mode == "RGB": image = image.convert("RGB") image = np.array(image).astype(np.uint8) print("upload image shape: {}".format(image.shape)) img = Image.fromarray(image) if resize: img = img.resize((256, 256)) image = np.array(img) return image def single_image_to_torch(x, permute=True): assert x is not None, "Please provide an image through the upload function" x = np.array(x) x = torch.FloatTensor(x/255.*2. - 1.)[None,...] if permute: x = x.permute(0, 3, 1, 2) return x def pad_to_M(x, M): hp = math.ceil(x.shape[2]/M)*M-x.shape[2] wp = math.ceil(x.shape[3]/M)*M-x.shape[3] x = torch.nn.functional.pad(x, (0,wp,0,hp,0,0,0,0)) return x @torch.no_grad() def run_conditional(model, dsets): if len(dsets.datasets) > 1: split = st.sidebar.radio("Split", sorted(dsets.datasets.keys())) dset = dsets.datasets[split] else: dset = next(iter(dsets.datasets.values())) batch_size = 1 start_index = st.sidebar.number_input("Example Index (Size: {})".format(len(dset)), value=0, min_value=0, max_value=len(dset)-batch_size) indices = list(range(start_index, start_index+batch_size)) example = default_collate([dset[i] for i in indices]) x = model.get_input("image", example).to(model.device) cond_key = model.cond_stage_key c = model.get_input(cond_key, example).to(model.device) scale_factor = st.sidebar.slider("Scale Factor", min_value=0.5, max_value=4.0, step=0.25, value=1.00) if scale_factor != 1.0: x = torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="bicubic") c = torch.nn.functional.interpolate(c, scale_factor=scale_factor, mode="bicubic") quant_z, z_indices = model.encode_to_z(x) quant_c, c_indices = model.encode_to_c(c) cshape = quant_z.shape xrec = model.first_stage_model.decode(quant_z) st.write("image: {}".format(x.shape)) st.image(bchw_to_st(x), clamp=True, output_format="PNG") st.write("image reconstruction: {}".format(xrec.shape)) st.image(bchw_to_st(xrec), clamp=True, output_format="PNG") if cond_key == "segmentation": # get image from segmentation mask num_classes = c.shape[1] c = torch.argmax(c, dim=1, keepdim=True) c = torch.nn.functional.one_hot(c, num_classes=num_classes) c = c.squeeze(1).permute(0, 3, 1, 2).float() c = model.cond_stage_model.to_rgb(c) st.write(f"{cond_key}: {tuple(c.shape)}") st.image(bchw_to_st(c), clamp=True, output_format="PNG") idx = z_indices half_sample = st.sidebar.checkbox("Image Completion", value=False) if half_sample: start = idx.shape[1]//2 else: start = 0 idx[:,start:] = 0 idx = idx.reshape(cshape[0],cshape[2],cshape[3]) start_i = start//cshape[3] start_j = start %cshape[3] if not half_sample and quant_z.shape == quant_c.shape: st.info("Setting idx to c_indices") idx = c_indices.clone().reshape(cshape[0],cshape[2],cshape[3]) cidx = c_indices cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3]) xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape) st.image(bchw_to_st(xstart), clamp=True, output_format="PNG") temperature = st.number_input("Temperature", value=1.0) top_k = st.number_input("Top k", value=100) sample = st.checkbox("Sample", value=True) update_every = st.number_input("Update every", value=75) st.text(f"Sampling shape ({cshape[2]},{cshape[3]})") animate = st.checkbox("animate") if animate: import imageio outvid = "sampling.mp4" writer = imageio.get_writer(outvid, fps=25) elapsed_t = st.empty() info = st.empty() st.text("Sampled") if st.button("Sample"): output = st.empty() start_t = time.time() for i in range(start_i,cshape[2]-0): if i <= 8: local_i = i elif cshape[2]-i < 8: local_i = 16-(cshape[2]-i) else: local_i = 8 for j in range(start_j,cshape[3]-0): if j <= 8: local_j = j elif cshape[3]-j < 8: local_j = 16-(cshape[3]-j) else: local_j = 8 i_start = i-local_i i_end = i_start+16 j_start = j-local_j j_end = j_start+16 elapsed_t.text(f"Time: {time.time() - start_t} seconds") info.text(f"Step: ({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})") patch = idx[:,i_start:i_end,j_start:j_end] patch = patch.reshape(patch.shape[0],-1) cpatch = cidx[:, i_start:i_end, j_start:j_end] cpatch = cpatch.reshape(cpatch.shape[0], -1) patch = torch.cat((cpatch, patch), dim=1) logits,_ = model.transformer(patch[:,:-1]) logits = logits[:, -256:, :] logits = logits.reshape(cshape[0],16,16,-1) logits = logits[:,local_i,local_j,:] logits = logits/temperature if top_k is not None: logits = model.top_k_logits(logits, top_k) # apply softmax to convert to probabilities probs = torch.nn.functional.softmax(logits, dim=-1) # sample from the distribution or take the most likely if sample: ix = torch.multinomial(probs, num_samples=1) else: _, ix = torch.topk(probs, k=1, dim=-1) idx[:,i,j] = ix if (i*cshape[3]+j)%update_every==0: xstart = model.decode_to_img(idx[:, :cshape[2], :cshape[3]], cshape,) xstart = bchw_to_st(xstart) output.image(xstart, clamp=True, output_format="PNG") if animate: writer.append_data((xstart[0]*255).clip(0, 255).astype(np.uint8)) xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape) xstart = bchw_to_st(xstart) output.image(xstart, clamp=True, output_format="PNG") #save_img(xstart, "full_res_sample.png") if animate: writer.close() st.video(outvid) def get_parser(): parser = argparse.ArgumentParser() parser.add_argument( "-r", "--resume", type=str, nargs="?", help="load from logdir or checkpoint in logdir", ) parser.add_argument( "-b", "--base", nargs="*", metavar="base_config.yaml", help="paths to base configs. Loaded from left-to-right. " "Parameters can be overwritten or added with command-line options of the form `--key value`.", default=list(), ) parser.add_argument( "-c", "--config", nargs="?", metavar="single_config.yaml", help="path to single config. If specified, base configs will be ignored " "(except for the last one if left unspecified).", const=True, default="", ) parser.add_argument( "--ignore_base_data", action="store_true", help="Ignore data specification from base configs. Useful if you want " "to specify a custom datasets on the command line.", ) return parser def load_model_from_config(config, sd, gpu=True, eval_mode=True): if "ckpt_path" in config.params: st.warning("Deleting the restore-ckpt path from the config...") config.params.ckpt_path = None if "downsample_cond_size" in config.params: st.warning("Deleting downsample-cond-size from the config and setting factor=0.5 instead...") config.params.downsample_cond_size = -1 config.params["downsample_cond_factor"] = 0.5 try: if "ckpt_path" in config.params.first_stage_config.params: config.params.first_stage_config.params.ckpt_path = None st.warning("Deleting the first-stage restore-ckpt path from the config...") if "ckpt_path" in config.params.cond_stage_config.params: config.params.cond_stage_config.params.ckpt_path = None st.warning("Deleting the cond-stage restore-ckpt path from the config...") except: pass model = instantiate_from_config(config) if sd is not None: missing, unexpected = model.load_state_dict(sd, strict=False) st.info(f"Missing Keys in State Dict: {missing}") st.info(f"Unexpected Keys in State Dict: {unexpected}") if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def get_data(config): # get data data = instantiate_from_config(config.data) data.prepare_data() data.setup() return data @st.cache(allow_output_mutation=True, suppress_st_warning=True) def load_model_and_dset(config, ckpt, gpu, eval_mode): # get data dsets = get_data(config) # calls data.config ... # now load the specified checkpoint if ckpt: pl_sd = torch.load(ckpt, map_location="cpu") global_step = pl_sd["global_step"] else: pl_sd = {"state_dict": None} global_step = None model = load_model_from_config(config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode)["model"] return dsets, model, global_step if __name__ == "__main__": sys.path.append(os.getcwd()) parser = get_parser() opt, unknown = parser.parse_known_args() ckpt = None if opt.resume: if not os.path.exists(opt.resume): raise ValueError("Cannot find {}".format(opt.resume)) if os.path.isfile(opt.resume): paths = opt.resume.split("/") try: idx = len(paths)-paths[::-1].index("logs")+1 except ValueError: idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt logdir = "/".join(paths[:idx]) ckpt = opt.resume else: assert os.path.isdir(opt.resume), opt.resume logdir = opt.resume.rstrip("/") ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") print(f"logdir:{logdir}") base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml"))) opt.base = base_configs+opt.base if opt.config: if type(opt.config) == str: opt.base = [opt.config] else: opt.base = [opt.base[-1]] configs = [OmegaConf.load(cfg) for cfg in opt.base] cli = OmegaConf.from_dotlist(unknown) if opt.ignore_base_data: for config in configs: if hasattr(config, "data"): del config["data"] config = OmegaConf.merge(*configs, cli) st.sidebar.text(ckpt) gs = st.sidebar.empty() gs.text(f"Global step: ?") st.sidebar.text("Options") #gpu = st.sidebar.checkbox("GPU", value=True) gpu = True #eval_mode = st.sidebar.checkbox("Eval Mode", value=True) eval_mode = True #show_config = st.sidebar.checkbox("Show Config", value=False) show_config = False if show_config: st.info("Checkpoint: {}".format(ckpt)) st.json(OmegaConf.to_container(config)) dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode) gs.text(f"Global step: {global_step}") run_conditional(model, dsets)