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import matplotlib.pyplot as plt
import math
import utils
from . import parse
save_ind = 0
def visualize(image, title, colorbar=False, show_plot=True, **kwargs):
plt.title(title)
plt.imshow(image, **kwargs)
if colorbar:
plt.colorbar()
if show_plot:
plt.show()
def visualize_arrays(image_title_pairs, colorbar_index=-1, show_plot=True, figsize=None, **kwargs):
if figsize is not None:
plt.figure(figsize=figsize)
num_subplots = len(image_title_pairs)
for idx, image_title_pair in enumerate(image_title_pairs):
plt.subplot(1, num_subplots, idx+1)
if isinstance(image_title_pair, (list, tuple)):
image, title = image_title_pair
else:
image, title = image_title_pair, None
if title is not None:
plt.title(title)
plt.imshow(image, **kwargs)
if idx == colorbar_index:
plt.colorbar()
if show_plot:
plt.show()
def visualize_masked_latents(latents_all, masked_latents, timestep_T=False, timestep_0=True):
if timestep_T:
# from T to 0
latent_idx = 0
plt.subplot(1, 2, 1)
plt.title("latents_all (t=T)")
plt.imshow((latents_all[latent_idx, 0, :3].cpu().permute(1,2,0).numpy().astype(float) / 1.5).clip(0., 1.), cmap="gray")
plt.subplot(1, 2, 2)
plt.title("mask latents (t=T)")
plt.imshow((masked_latents[latent_idx, 0, :3].cpu().permute(1,2,0).numpy().astype(float) / 1.5).clip(0., 1.), cmap="gray")
plt.show()
if timestep_0:
latent_idx = -1
plt.subplot(1, 2, 1)
plt.title("latents_all (t=0)")
plt.imshow((latents_all[latent_idx, 0, :3].cpu().permute(1,2,0).numpy().astype(float) / 1.5).clip(0., 1.), cmap="gray")
plt.subplot(1, 2, 2)
plt.title("mask latents (t=0)")
plt.imshow((masked_latents[latent_idx, 0, :3].cpu().permute(1,2,0).numpy().astype(float) / 1.5).clip(0., 1.), cmap="gray")
plt.show()
# This function has not been adapted to new `saved_attn`.
def visualize_attn(token_map, cross_attention_probs_tensors, stage_id, block_id, visualize_step_start=10, input_ca_has_condition_only=False):
"""
Visualize cross attention: `stage_id`th downsampling block, mean over all timesteps starting from step start, `block_id`th Transformer block, second item (conditioned), mean over heads, show each token
cross_attention_probs_tensors:
One of `cross_attention_probs_down_tensors`, `cross_attention_probs_mid_tensors`, and `cross_attention_probs_up_tensors`
stage_id: index of downsampling/mid/upsaming block
block_id: index of the transformer block
"""
plt.figure(figsize=(20, 8))
for token_id in range(len(token_map)):
token = token_map[token_id]
plt.subplot(1, len(token_map), token_id + 1)
plt.title(token)
attn = cross_attention_probs_tensors[stage_id][visualize_step_start:].mean(dim=0)[block_id]
if not input_ca_has_condition_only:
assert attn.shape[0] == 2, f"Expect to have 2 items (uncond and cond), but found {attn.shape[0]} items"
attn = attn[1]
else:
assert attn.shape[0] == 1, f"Expect to have 1 item (cond only), but found {attn.shape[0]} items"
attn = attn[0]
attn = attn.mean(dim=0)[:, token_id]
H = W = int(math.sqrt(attn.shape[0]))
attn = attn.reshape((H, W))
plt.imshow(attn.cpu().numpy())
plt.show()
# This function has not been adapted to new `saved_attn`.
def visualize_across_timesteps(token_id, cross_attention_probs_tensors, stage_id, block_id, visualize_step_start=10, input_ca_has_condition_only=False):
"""
Visualize cross attention for one token, across timesteps: `stage_id`th downsampling block, mean over all timesteps starting from step start, `block_id`th Transformer block, second item (conditioned), mean over heads, show each token
cross_attention_probs_tensors:
One of `cross_attention_probs_down_tensors`, `cross_attention_probs_mid_tensors`, and `cross_attention_probs_up_tensors`
stage_id: index of downsampling/mid/upsaming block
block_id: index of the transformer block
`visualize_step_start` is not used. We visualize all timesteps.
"""
plt.figure(figsize=(50, 8))
attn_stage = cross_attention_probs_tensors[stage_id]
num_inference_steps = attn_stage.shape[0]
for t in range(num_inference_steps):
plt.subplot(1, num_inference_steps, t + 1)
plt.title(f"t: {t}")
attn = attn_stage[t][block_id]
if not input_ca_has_condition_only:
assert attn.shape[0] == 2, f"Expect to have 2 items (uncond and cond), but found {attn.shape[0]} items"
attn = attn[1]
else:
assert attn.shape[0] == 1, f"Expect to have 1 item (cond only), but found {attn.shape[0]} items"
attn = attn[0]
attn = attn.mean(dim=0)[:, token_id]
H = W = int(math.sqrt(attn.shape[0]))
attn = attn.reshape((H, W))
plt.imshow(attn.cpu().numpy())
plt.axis("off")
plt.tight_layout()
plt.show()
def visualize_bboxes(bboxes, H, W):
num_boxes = len(bboxes)
for ind, bbox in enumerate(bboxes):
plt.subplot(1, num_boxes, ind + 1)
fg_mask = utils.proportion_to_mask(bbox, H, W)
plt.title(f"transformed bbox ({ind})")
plt.imshow(fg_mask.cpu().numpy())
plt.show()
def display(image, save_prefix="", ind=None):
global save_ind
if save_prefix != "":
save_prefix = save_prefix + "_"
ind = f"{ind}_" if ind is not None else ""
path = f"{parse.img_dir}/{save_prefix}{ind}{save_ind}.png"
print(f"Saved to {path}")
image.save(path)
save_ind = save_ind + 1
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