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"docs/vscode:/vscode.git/clone" did not exist on "5383666961f3abef0c10bc462e8c2f2b3d357899"
Commit 031cd626 authored by mashun1's avatar mashun1
Browse files

taming-transformers

parent 01db7703
Show whitespace changes
Inline Side-by-side
Showing with 2636 additions and 1 deletion
.gitignore
View file @ 031cd626
......@@ -6,5 +6,5 @@ nohup*
logs/
results
*.egg*
sample*
sample_for_test
imagenet_depth
\ No newline at end of file
configs/faceshq_vqgan.yaml
View file @ 031cd626
......@@ -40,3 +40,7 @@ data:
params:
size: 256
crop_size: 256
lightning:
trainer:
max_epochs: 2
\ No newline at end of file
sample.txt 0 → 100644
View file @ 031cd626
nohup: ignoring input
logdir:logs/2024-02-19T08-49-48_faceshq_transformer
Working with z of shape (1, 256, 16, 16) = 65536 dimensions.
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scripts/sample_conditional.py 0 → 100644
View file @ 031cd626
import argparse, os, sys, glob, math, time
import torch
import numpy as np
from omegaconf import OmegaConf
from pathlib import Path
import sys
parent_dir = Path(__file__).resolve()
parent_dir = parent_dir.parent.parent
sys.path.append(str(parent_dir))
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)
scripts/sample_fast.py 0 → 100644
View file @ 031cd626
import argparse, os, sys, glob
import torch
import time
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from einops import repeat
from main import instantiate_from_config
from taming.modules.transformer.mingpt import sample_with_past
rescale = lambda x: (x + 1.) / 2.
def chw_to_pillow(x):
return Image.fromarray((255*rescale(x.detach().cpu().numpy().transpose(1,2,0))).clip(0,255).astype(np.uint8))
@torch.no_grad()
def sample_classconditional(model, batch_size, class_label, steps=256, temperature=None, top_k=None, callback=None,
dim_z=256, h=16, w=16, verbose_time=False, top_p=None):
log = dict()
assert type(class_label) == int, f'expecting type int but type is {type(class_label)}'
qzshape = [batch_size, dim_z, h, w]
assert not model.be_unconditional, 'Expecting a class-conditional Net2NetTransformer.'
c_indices = repeat(torch.tensor([class_label]), '1 -> b 1', b=batch_size).to(model.device) # class token
t1 = time.time()
index_sample = sample_with_past(c_indices, model.transformer, steps=steps,
sample_logits=True, top_k=top_k, callback=callback,
temperature=temperature, top_p=top_p)
if verbose_time:
sampling_time = time.time() - t1
print(f"Full sampling takes about {sampling_time:.2f} seconds.")
x_sample = model.decode_to_img(index_sample, qzshape)
log["samples"] = x_sample
log["class_label"] = c_indices
return log
@torch.no_grad()
def sample_unconditional(model, batch_size, steps=256, temperature=None, top_k=None, top_p=None, callback=None,
dim_z=256, h=16, w=16, verbose_time=False):
log = dict()
qzshape = [batch_size, dim_z, h, w]
assert model.be_unconditional, 'Expecting an unconditional model.'
c_indices = repeat(torch.tensor([model.sos_token]), '1 -> b 1', b=batch_size).to(model.device) # sos token
t1 = time.time()
index_sample = sample_with_past(c_indices, model.transformer, steps=steps,
sample_logits=True, top_k=top_k, callback=callback,
temperature=temperature, top_p=top_p)
if verbose_time:
sampling_time = time.time() - t1
print(f"Full sampling takes about {sampling_time:.2f} seconds.")
x_sample = model.decode_to_img(index_sample, qzshape)
log["samples"] = x_sample
return log
@torch.no_grad()
def run(logdir, model, batch_size, temperature, top_k, unconditional=True, num_samples=50000,
given_classes=None, top_p=None):
batches = [batch_size for _ in range(num_samples//batch_size)] + [num_samples % batch_size]
if not unconditional:
assert given_classes is not None
print("Running in pure class-conditional sampling mode. I will produce "
f"{num_samples} samples for each of the {len(given_classes)} classes, "
f"i.e. {num_samples*len(given_classes)} in total.")
for class_label in tqdm(given_classes, desc="Classes"):
for n, bs in tqdm(enumerate(batches), desc="Sampling Class"):
if bs == 0: break
logs = sample_classconditional(model, batch_size=bs, class_label=class_label,
temperature=temperature, top_k=top_k, top_p=top_p)
save_from_logs(logs, logdir, base_count=n * batch_size, cond_key=logs["class_label"])
else:
print(f"Running in unconditional sampling mode, producing {num_samples} samples.")
for n, bs in tqdm(enumerate(batches), desc="Sampling"):
if bs == 0: break
logs = sample_unconditional(model, batch_size=bs, temperature=temperature, top_k=top_k, top_p=top_p)
save_from_logs(logs, logdir, base_count=n * batch_size)
def save_from_logs(logs, logdir, base_count, key="samples", cond_key=None):
xx = logs[key]
for i, x in enumerate(xx):
x = chw_to_pillow(x)
count = base_count + i
if cond_key is None:
x.save(os.path.join(logdir, f"{count:06}.png"))
else:
condlabel = cond_key[i]
if type(condlabel) == torch.Tensor: condlabel = condlabel.item()
os.makedirs(os.path.join(logdir, str(condlabel)), exist_ok=True)
x.save(os.path.join(logdir, str(condlabel), f"{count:06}.png"))
def get_parser():
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser()
parser.add_argument(
"-r",
"--resume",
type=str,
nargs="?",
help="load from logdir or checkpoint in logdir",
)
parser.add_argument(
"-o",
"--outdir",
type=str,
nargs="?",
help="path where the samples will be logged to.",
default=""
)
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(
"-n",
"--num_samples",
type=int,
nargs="?",
help="num_samples to draw",
default=50000
)
parser.add_argument(
"--batch_size",
type=int,
nargs="?",
help="the batch size",
default=25
)
parser.add_argument(
"-k",
"--top_k",
type=int,
nargs="?",
help="top-k value to sample with",
default=250,
)
parser.add_argument(
"-t",
"--temperature",
type=float,
nargs="?",
help="temperature value to sample with",
default=1.0
)
parser.add_argument(
"-p",
"--top_p",
type=float,
nargs="?",
help="top-p value to sample with",
default=1.0
)
parser.add_argument(
"--classes",
type=str,
nargs="?",
help="specify comma-separated classes to sample from. Uses 1000 classes per default.",
default="imagenet"
)
return parser
def load_model_from_config(config, sd, gpu=True, eval_mode=True):
model = instantiate_from_config(config)
if sd is not None:
model.load_state_dict(sd)
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def load_model(config, ckpt, gpu, eval_mode):
# load the specified checkpoint
if ckpt:
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
print(f"loaded model from global step {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 model, global_step
if __name__ == "__main__":
sys.path.append(os.getcwd())
parser = get_parser()
opt, unknown = parser.parse_known_args()
assert opt.resume
ckpt = None
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")
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
print(base_configs)
opt.base = base_configs+opt.base
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
model, global_step = load_model(config, ckpt, gpu=True, eval_mode=True)
if opt.outdir:
print(f"Switching logdir from '{logdir}' to '{opt.outdir}'")
logdir = opt.outdir
if opt.classes == "imagenet":
given_classes = [i for i in range(1000)]
else:
cls_str = opt.classes
assert not cls_str.endswith(","), 'class string should not end with a ","'
given_classes = [int(c) for c in cls_str.split(",")]
logdir = os.path.join(logdir, "samples", f"top_k_{opt.top_k}_temp_{opt.temperature:.2f}_top_p_{opt.top_p}",
f"{global_step}")
print(f"Logging to {logdir}")
os.makedirs(logdir, exist_ok=True)
run(logdir, model, opt.batch_size, opt.temperature, opt.top_k, unconditional=model.be_unconditional,
given_classes=given_classes, num_samples=opt.num_samples, top_p=opt.top_p)
print("done.")
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