import os import time import argparse import wandb import random import numpy as np import tensorflow as tf from custom_utils.custom_utils import UV2Mesh, maskFacesBatch from tensorboardX import SummaryWriter import plotly.graph_objects as go import plotly.express as ex from utils import str_to_bool from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing from load_models import load_generator, load_discriminator from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss # from losses import collision_penalty as collision_penalty from losses import collision_loss as collision_penalty from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block from stylegan2_ae.generator import Generator from custom_utils.graph_utils import Reconstructor_V4_Decoder as GNNDecoder from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor from dataset import DatasetFactory, StaticDataset from custom_utils.custom_utils import batch_to_dict, plot_3d from custom_utils.smpl import SMPL name='ae_body_1' # os.environ["CUDA_VISIBLE_DEVICES"] = "6" # run = wandb.init(project='stylegan', name=name, # notes='convnext + convnext, add dense with tanh at the end of reverse convnext, larger features. add lr scheduler. fft3d instead of rfft3d. \ # fft2d per channel and separate for top and bottom. fft_loss, all data. added convnext block as bottleneck, same as before, added extra metric. \ # . lowered learning rate because it goes to nan. shallow third stage. slower lr decay. with validation. higher lr rate.') # artifact = wandb.Artifact('code', type='code') # artifact.add_file('auto_encoder.py') # artifact.add_file('stylegan2_ae/layers/mapping_block.py') # artifact.add_file('convnext/models/convnext_tf.py') # run.log_artifact(artifact) uv2mesh = UV2Mesh(res=256) class DatasetSimple(tf.data.Dataset): def _generator(folder): # Opening the file folder = folder.decode("utf-8") uv_folder = os.path.join(folder, 'uv_posed') frames = os.listdir(uv_folder) random.shuffle(frames) for frame in frames: id, frame_nr = frame.split('.')[0].split('_') mesh_mask = np.load(os.path.join(folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32) uv_mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32) mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed', id + '_' + frame_nr + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed'] uv_unposed = np.load(os.path.join(folder, 'uv_unposed', id + '_' + frame_nr + '.npz')); uv_unposed = uv_unposed['uv_unposed']; uv_unposed = np.transpose(uv_unposed, [2, 0, 1]) yield uv_unposed, mesh_unposed, uv_mask, mesh_mask def __new__(cls, folder, res=256): return tf.data.Dataset.from_generator( cls._generator, output_signature = (tf.TensorSpec(shape = (3, res * 2, res,), dtype = tf.float32), tf.TensorSpec(shape = (14475, 3,), dtype = tf.float32), tf.TensorSpec(shape = (res * 2, res,), dtype = tf.float32), tf.TensorSpec(shape = (14475,), dtype = tf.float32), ), args=(folder, ) ) def filter_resolutions_featuremaps(resolutions, featuremaps, res): index = resolutions.index(res) filtered_resolutions = resolutions[:index + 1] filtered_featuremaps = featuremaps[:index + 1] return filtered_resolutions, filtered_featuremaps # def plot_3d(mesh_gt, mesh_pred, faces): # mesh_pred[:,1] += 2 # fig = go.Figure(data=[ # go.Mesh3d( # # 8 vertices of a cube # x=mesh_gt[:,0], # y=mesh_gt[:,1], # z=mesh_gt[:,2], # # i, j and k give the vertices of triangles # i = faces[:,0], # j = faces[:,1], # k = faces[:,2] # ), # go.Mesh3d( # # 8 vertices of a cube # x=mesh_pred[:,0], # y=mesh_pred[:,1], # z=mesh_pred[:,2], # # i, j and k give the vertices of triangles # i = faces[:,0], # j = faces[:,1], # k = faces[:,2], # ) # ]) # fig.update_layout(scene_aspectmode='manual', # scene_aspectratio=dict(x=1, y=2, z=1)) # fig.update_layout( # scene = dict( # xaxis = dict(nticks=4, range=[-1,1],), # yaxis = dict(nticks=4, range=[-1,3],), # zaxis = dict(nticks=4, range=[-1,1],),) # ) # return fig class Bottleneck(tf.keras.Model): def __init__(self, dim): super(Bottleneck, self).__init__() self.bottleneck = tf.keras.Sequential( [Block(dim=dim, drop_path=0, layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)] ) def call(self, inputs, training=None): x = inputs x = self.bottleneck(x, training=training) return x class AutoEncoder(tf.keras.Model): def __init__(self): super(AutoEncoder, self).__init__() bottleneck_size = 32 self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, bottleneck_size], include_top=False) self.bottleneck = Bottleneck(bottleneck_size) self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16]))) def call(self, inputs, training=None): x, uv_mask = inputs x = tf.transpose(x, [0,2,3,1]) x = self.encoder(x, training=training) # x = self.pooling(x) # x = self.reshape(x) # x = self.flatten(x) x = self.bottleneck(x, training=training) x = self.decoder(x, training=training) x = tf.transpose(x, [0,3,1,2]) # x = x * uv_mask[:,:, tf.newaxis] x = x * uv_mask[:,tf.newaxis,:,:] return x def draw_cicle(shape,diamiter): ''' Input: shape : tuple (height, width) diameter : scalar Output: np.array of shape that says True within a circle with diamiter = around center ''' assert len(shape) == 2 TF = np.zeros(shape,dtype=bool) center = np.array(TF.shape)/2.0 for iy in range(shape[0]): for ix in range(shape[1]): TF[iy,ix] = (iy- center[0])**2 + (ix - center[1])**2 < diamiter **2 return (TF) TFcircleIN = draw_cicle(shape=(256,256), diamiter=15) TFcircleIN = np.ones((512,256,3), dtype=bool) * np.concatenate((TFcircleIN, TFcircleIN), axis=0)[:,:,np.newaxis] TFcircleOUT = ~TFcircleIN def fft_loss_per_channel(pred, gt): pred = tf.complex(pred, tf.zeros_like(pred)) gt = tf.complex(gt, tf.zeros_like(gt)) fft_pred_top = tf.stack([tf.signal.fftshift(tf.signal.fft2d(pred[:,i,:256,:])) for i in range(3)], axis=-1) fft_gt_top = tf.stack([tf.signal.fftshift(tf.signal.fft2d(gt[:,i,:256,:])) for i in range(3)], axis=-1) fft_pred_bottom = tf.stack([tf.signal.fftshift(tf.signal.fft2d(pred[:,i,256:,:])) for i in range(3)], axis=-1) fft_gt_bottom = tf.stack([tf.signal.fftshift(tf.signal.fft2d(gt[:,i,256:,:])) for i in range(3)], axis=-1) fft_pred = tf.concat((fft_pred_top, fft_pred_bottom), axis=1) fft_gt = tf.concat((fft_gt_top, fft_gt_bottom), axis=1) fft_pred_low = tf.where(TFcircleIN, fft_pred, tf.zeros_like(fft_pred)) fft_gt_low = tf.where(TFcircleIN, fft_gt, tf.zeros_like(fft_gt)) fft_pred_high = tf.where(TFcircleOUT, fft_pred, tf.zeros_like(fft_pred)) fft_gt_high = tf.where(TFcircleOUT, fft_gt, tf.zeros_like(fft_gt)) low_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred_low - fft_gt_low), axis=[1,2,3])) high_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred_high - fft_gt_high), axis=[1,2,3])) total = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred - fft_gt), axis=[1,2,3])) return total, low_loss, high_loss # ex.imshow(tf.math.real(gt[0,0,:256,:])).show() # img = tf.signal.fftshift(fft_gt[0][0]) # ex.imshow(tf.math.abs(tf.math.log(img))).show() # low = tf.where(TFcircleIN, img, tf.zeros_like(img)) # ex.imshow(tf.math.real(tf.signal.ifft2d(tf.signal.ifftshift(low)))).show() # high = tf.where(TFcircleOUT, img, tf.zeros_like(img)) # # ex.imshow(tf.math.abs(tf.math.log(high))).show() # ex.imshow(tf.math.real(tf.signal.ifft2d(tf.signal.ifftshift(high)))).show() print() @tf.function def train_step(batch): with tf.GradientTape() as tape: pred = model([batch['uv_unposed'], batch['uv_mask']]) # pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis] mesh_pred = uv2mesh(pred) # mesh_pred = pred mesh_orig = batch['mesh_unposed'] e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask'])) # l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['mesh_unposed'] - pred)), axis=-1) * batch['mesh_mask']) l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=1) * batch['uv_mask']) ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.transpose(pred, [0,2,3,1]) + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2)) rfft_loss = 0 #tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.rfft3d(pred) - tf.signal.rfft3d(batch['uv_unposed'])), axis=[1,2,3])) fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3])) fft_loss_pc, fft_loss_pc_low, fft_loss_pc_high = 0,0,0 #fft_loss_per_channel(pred, batch['uv_unposed']) # loss = fft_loss_pc # + e_loss + c_loss loss = 1e3 * (1 - ms_ssim) mesh_orig_2 = uv2mesh(batch['uv_unposed']) # mesh_skinned = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred) metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) euclid_posed = 0 # tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_skinned - batch['mesh_posed']) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) gradients = tape.gradient(loss, model.trainable_variables) # gradients = [tf.clip_by_norm(g, 1.0) for g in gradients] # gradients = [tf.clip_by_value(g, -1.0, 1.0) for g in gradients] optimizer.apply_gradients(zip(gradients, model.trainable_variables)) return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, 'train/fft_loss': fft_loss, 'train/euclid_error_reconst': metric_2, 'train/rfft_loss': rfft_loss, 'train/fft_oc_low': fft_loss_pc_low, 'train/fft_pc_high': fft_loss_pc_high, 'train/fft_loss_per_channel': fft_loss_pc, 'train/euclid_error_posed': euclid_posed } @tf.function def valid_step(batch): pred = model([batch['uv_unposed'], batch['uv_mask']]) mesh_pred = uv2mesh(pred) mesh_orig = batch['mesh_unposed'] e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask'])) l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=1) * batch['uv_mask']) ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.transpose(pred, [0,2,3,1]) + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2)) fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3])) loss = 1e3 * (1 - ms_ssim) mesh_orig_2 = uv2mesh(batch['uv_unposed']) metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, 'valid/fft_loss': fft_loss, 'valid/euclid_error_reconst': metric_2 } def plot_everything(batch: dict, type: str): pred = model([batch['uv_unposed'], batch['uv_mask']]) mesh_pred = uv2mesh(pred) mesh_orig = batch['mesh_unposed'] orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1]) pred_img = tf.transpose(pred, [0,2,3,1]) img = tf.concat((orig_img, pred_img), axis=2) imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)] F = np.load("custom_utils/smpl_faces.npy") mask = batch['mesh_mask'].numpy().astype(bool) garment_unposed, faces = maskFacesBatch(mesh_pred.numpy(), F, mask) gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])] body = batch['mesh_body_posed'].numpy() mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy() garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])] collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))] collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))] collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))] figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) } return { **figs, **{ type + '/images': imgs } } if __name__ == '__main__': allow_memory_growth() parser = argparse.ArgumentParser(description='') parser.add_argument('--name', type=str) args = vars(parser.parse_args()) smpl = SMPL() # R = smpl.run_rodrigues((np.zeros((8,72), dtype=np.float32))) # zero_body = smpl.run(R, np.zeros((8, 11), dtype=np.float32))[0] factory = DatasetFactory() signature_dynamic = { 'id': (), 'pose_shape': (72 + 11,), # 'uv_posed': (3, 512, 256,), 'uv_body_posed': (3, 512, 256,), 'mesh_body_posed': (14475, 3,), 'mesh_posed': (14475, 3,), 'mesh_unposed': (14475, 3,), # 'uv_unposed': (3, 512, 256,) } signature_static = { #'uv_static': (3, 512, 256,), # 'mesh_static': (14475, 3,), 'mesh_mask': (14475,), 'uv_mask': (512, 256,), # 'uv_shaped_body': (3, 512, 256,), } dataset_train = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train', batch_size=8, signature=signature_dynamic) static_dataset_train = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train', signature=signature_static) dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=8, signature=signature_dynamic) static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static) model = AutoEncoder() # optimizer = tf.optimizers.Adam(learning_rate=0.002, beta_1=0.0, beta_2=0.99, epsilon=1e-8) lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0005, decay_steps=30000, decay_rate=0.1, staircase=False) optimizer = tf.optimizers.Adam(learning_rate=lr_schedule) folder = 'models/ae_logs/' # train_summary_writer = tf.summary.create_file_writer(folder + name + '/train') # valid_summary_writer = tf.summary.create_file_writer(folder + name + '/valid') valid_iterator = iter(dataset_valid) step = 0 for batch in dataset_train: batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) train_loss = train_step(batch) data_valid = valid_iterator.get_next() data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic)) valid_loss = valid_step(data_valid) if step % 1000 == 0: train_figs = plot_everything(batch, 'train') valid_figs = plot_everything(data_valid, 'valid') wandb.log({ **train_loss, **valid_loss, **train_figs, **valid_figs }, step=step) else: wandb.log({ **train_loss, **valid_loss }, step=step) print(step) step += 1