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import os
import torch
import itertools
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch.nn.functional as F
# from contextlib import contextmanager
# from ldm.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
# from ldm.modules.diffusionmodules.model import Encoder, Decoder
# from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModel, AutoencoderKL
from ldm.models.disentanglement.iterative_normalization import IterNormRotation as cw_layer
from ldm.analysis_utils import get_CosineDistance_matrix, aggregatefrom_specimen_to_species
from ldm.plotting_utils import plot_heatmap_at_path
from ldm.util import instantiate_from_config
CONCEPT_DATA_KEY = "concept_data"
class CWmodelVQGAN(VQModel):
def __init__(self, **args):
print(args)
self.save_hyperparameters()
concept_data_args = args[CONCEPT_DATA_KEY]
print("Concepts params : ", concept_data_args)
self.concepts = instantiate_from_config(concept_data_args)
self.concepts.prepare_data()
self.concepts.setup()
del args[CONCEPT_DATA_KEY]
super().__init__(**args)
if not self.cw_module_infer:
self.encoder.norm_out = cw_layer(self.encoder.block_in)
print("Changed to cw layer after loading base VQGAN")
def training_step(self, batch, batch_idx, optimizer_idx):
if (batch_idx+1)%30==0 and optimizer_idx==0:
print('cw module')
self.eval()
with torch.no_grad():
for _, concept_batch in enumerate(self.concepts.train_dataloader()):
for idx, concept in enumerate(concept_batch['class'].unique()):
concept_index = concept.item()
self.encoder.norm_out.mode = concept_index
X_var = concept_batch['image'][concept_batch['class'] == concept]
X_var = X_var.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
X_var = torch.autograd.Variable(X_var).cuda()
X_var = X_var.float()
self(X_var)
break
self.encoder.norm_out.update_rotation_matrix()
self.encoder.norm_out.mode = -1
self.train()
# breakpoint()
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x, return_pred_indices=False)
# if optimizer_idx == 0 or (not self.loss.has_discriminator):
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("train/aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return aeloss
# if optimizer_idx == 1 and self.loss.has_discriminator:
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("train/discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return discloss
@torch.no_grad()
def test_step(self, batch, batch_idx):
x = self.get_input(batch, self.image_key)
h = self.encoder(x)
h = self.quant_conv(h)
class_label = batch['class']
return {'z_cw': h,
'label': class_label,
'class_name': batch['class_name']}
# NOTE: This is kinda hacky. But ok for now for test purposes.
def set_test_chkpt_path(self, chkpt_path):
self.test_chkpt_path = chkpt_path
@torch.no_grad()
def test_epoch_end(self, in_out):
postfix_name = 'inference_false'
z_cw =torch.cat([x['z_cw'] for x in in_out], 0)
labels =torch.cat([x['label'] for x in in_out], 0)
sorting_indices = np.argsort(labels.cpu())
sorted_zq_cw = z_cw[sorting_indices, :]
classnames = list(itertools.chain.from_iterable([x['class_name'] for x in in_out]))
sorted_class_names_according_to_class_indx = [classnames[i] for i in sorting_indices]
z_size = sorted_zq_cw.shape[-1]
channels = sorted_zq_cw.shape[1]
# breakpoint()
figs_folder = os.path.join('/', *self.test_chkpt_path.split('/')[:-2], 'figs/testset_agg')
if not os.path.exists(figs_folder):
os.makedirs(figs_folder)
sorted_zq_cw_aggregated = aggregatefrom_specimen_to_species(sorted_class_names_according_to_class_indx, sorted_zq_cw, z_size, channels)
z_cosine_distances = get_CosineDistance_matrix(sorted_zq_cw_aggregated)
plot_heatmap_at_path(z_cosine_distances.cpu(), figs_folder, self.test_chkpt_path, title=f'Cosine_distances_{postfix_name}', postfix='testset_agg')
z_cosine_distancess_np = z_cosine_distances.cpu().numpy()
df = pd.DataFrame(z_cosine_distancess_np)
df = df.drop(columns=[5, 6])
df = df.drop([5, 6])
breakpoint()
path_to_save = os.path.join(figs_folder, f'CW_z_cosine_distances_{postfix_name}.csv')
print("saved to path : ", path_to_save)
df.to_csv(path_to_save)
return None
class CWmodelInterface(VQModel):
def __init__(self, **args):
print(args)
self.save_hyperparameters()
concept_data_args = args[CONCEPT_DATA_KEY]
print("Concepts params : ", concept_data_args)
self.concepts = instantiate_from_config(concept_data_args)
self.concepts.prepare_data()
self.concepts.setup()
del args[CONCEPT_DATA_KEY]
super().__init__(**args)
if not self.cw_module_infer:
self.encoder.norm_out = cw_layer(self.encoder.block_in)
print("Changed to cw layer after loading base VQGAN")
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, h, force_not_quantize=False):
# also go through quantization layer
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
class CWmodelKL(AutoencoderKL):
def __init__(self, **args):
print(args)
self.save_hyperparameters()
concept_data_args = args[CONCEPT_DATA_KEY]
print("Concepts params : ", concept_data_args)
self.concepts = instantiate_from_config(concept_data_args)
self.concepts.prepare_data()
self.concepts.setup()
del args[CONCEPT_DATA_KEY]
super().__init__(**args)
if not self.cw_module_infer:
self.encoder.norm_out = cw_layer(self.encoder.block_in)
print("Changed to cw layer after loading base KL Autoecoder")
def training_step(self, batch, batch_idx, optimizer_idx):
if (batch_idx+1)%30==0 and optimizer_idx==0:
print('cw module')
self.eval()
with torch.no_grad():
for _, concept_batch in enumerate(self.concepts.train_dataloader()):
for idx, concept in enumerate(concept_batch['class'].unique()):
concept_index = concept.item()
self.encoder.norm_out.mode = concept_index
X_var = concept_batch['image'][concept_batch['class'] == concept]
X_var = X_var.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
X_var = torch.autograd.Variable(X_var).cuda()
X_var = X_var.float()
self(X_var)
break
self.encoder.norm_out.update_rotation_matrix()
self.encoder.norm_out.mode = -1
self.train()
# breakpoint()
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return discloss
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