def call_plot(model_dict, data_dict, info):
from acn_utils import tsne_plot
from acn_utils import pca_plot
# always be in eval mode
model_dict = set_model_mode(model_dict, 'valid')
with torch.no_grad():
for phase in ['valid', 'train']:
data_loader = data_dict[phase]
data_loader.reset_unique()
batch = data_loader.get_unique_minibatch(84)
states, actions, rewards, next_states, _, _, batch_indexes, index_indexes = batch
fp_out = forward_pass(model_dict, states, actions, rewards, next_states, index_indexes, phase, info)
model_dict, states, actions, rewards, target, u_q, u_p, s_p, rec_dml, pcnn_dml, z_e_x, z_q_x, latents = fp_out
bs = states.shape[0]
u_q_flat = u_q.view(bs, info['code_length'])
X = u_q_flat.cpu().numpy()
color = index_indexes
images = target[:,0].cpu().numpy()
if args.tsne:
param_name = '_tsne_%s_P%s.html'%(phase, info['perplexity'])
html_path = info['model_loadpath'].replace('.pt', param_name)
tsne_plot(X=X, images=images, color=color,
perplexity=info['perplexity'],
html_out_path=html_path, serve=False)
if args.pca:
param_name = '_pca_%s.html'%(phase)
html_path = info['model_loadpath'].replace('.pt', param_name)
pca_plot(X=X, images=images, color=color,
html_out_path=html_path, serve=False)
break
def call_plot(model_dict, data_dict, info, sample, tsne, pca):
from acn_utils import tsne_plot
from acn_utils import pca_plot
# always be in eval mode - so we dont swap neighbors
model_dict = set_model_mode(model_dict, 'valid')
with torch.no_grad():
for phase in ['valid', 'train']:
data_loader = data_dict[phase]
data_loader.reset_unique()
batch = data_loader.get_unique_minibatch(84)
states, actions, rewards, next_states, _, _, batch_indexes, index_indexes = batch
fp_out = forward_pass(model_dict, states, actions, rewards,
next_states, index_indexes, phase, info)
model_dict, states, actions, rewards, target, u_q, u_p, s_p, rec_dml, z_e_x, z_q_x, latents = fp_out
rec_yhat = sample_from_discretized_mix_logistic(
rec_dml,
info['nr_logistic_mix'],
only_mean=info['sample_mean'],
sampling_temperature=info['sampling_temperature'])
f, ax = plt.subplots(10, 3)
ax[0, 0].set_title('prev')
ax[0, 1].set_title('true')
ax[0, 2].set_title('pred')
for i in range(10):
ax[i, 0].matshow(states[i, -1])
ax[i, 1].matshow(target[i, -1])
ax[i, 2].matshow(rec_yhat[i, -1])
ax[i, 0].axis('off')
ax[i, 1].axis('off')
ax[i, 2].axis('off')
plt.subplots_adjust(wspace=0, hspace=0)
plt.tight_layout()
plt_path = info['model_loadpath'].replace('.pt',
'_%s_plt.png' % phase)
print('plotting', plt_path)
plt.savefig(plt_path)
bs = states.shape[0]
u_q_flat = u_q.view(bs, info['code_length'])
X = u_q_flat.cpu().numpy()
color = index_indexes
images = target[:, 0].cpu().numpy()
if tsne:
param_name = '_tsne_%s_P%s.html' % (phase, info['perplexity'])
html_path = info['model_loadpath'].replace('.pt', param_name)
tsne_plot(X=X,
images=images,
color=color,
perplexity=info['perplexity'],
html_out_path=html_path,
serve=False)
if pca:
param_name = '_pca_%s.html' % (phase)
html_path = info['model_loadpath'].replace('.pt', param_name)
pca_plot(X=X,
images=images,
color=color,
html_out_path=html_path,
serve=False)