From ee8f1cb68ce13a9675cda1195b15ab0bac6038f1 Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Sat, 6 Aug 2022 12:27:24 +0000 Subject: [PATCH] lot of things --- .gitignore | 3 +- auto_encoder.py | 366 ++++++++++--- auto_encoder_body.py | 435 +++++++++++++++ auto_encoder_curv.py | 379 +++++++++++++ auto_encoder_static.py | 450 ++++++++++++++++ convnext/models/convnext_tf.py | 324 +++++++++++- convunext.py | 487 +++++++++++++++++ custom_data/jumpsuit_20_avg_uv.npy | Bin 0 -> 1572992 bytes custom_utils/{__init__.py => a__init__.py} | 0 custom_utils/custom_utils.py | 49 ++ custom_utils/graph_utils.py | 93 +++- custom_utils/laplacian/laplacian0.npz | Bin 0 -> 188972 bytes custom_utils/laplacian/laplacian5.npz | Bin 0 -> 20375 bytes custom_utils/smpl_faces_skirt.npy | Bin 0 -> 345464 bytes cvae.py | 584 +++++++++++++++++++++ dataset.py | 270 ++++++++-- docker/Dockerfile | 2 +- docker/requierements.txt | 3 +- downscaling_test.py | 259 +++++++++ downscaling_test_curv.py | 227 ++++++++ load_models.py | 19 +- losses.py | 56 ++ stylegan2/generator.py | 92 ++++ stylegan2/layers/embedding.py | 2 +- train.py | 30 +- vae.py | 414 +++++++++++++++ verify_scales.py | 81 +++ verify_uv_mapping.py | 145 +++++ 28 files changed, 4595 insertions(+), 175 deletions(-) create mode 100644 auto_encoder_body.py create mode 100644 auto_encoder_curv.py create mode 100644 auto_encoder_static.py create mode 100644 convunext.py create mode 100644 custom_data/jumpsuit_20_avg_uv.npy rename custom_utils/{__init__.py => a__init__.py} (100%) create mode 100644 custom_utils/laplacian/laplacian0.npz create mode 100644 custom_utils/laplacian/laplacian5.npz create mode 100644 custom_utils/smpl_faces_skirt.npy create mode 100644 cvae.py create mode 100644 downscaling_test.py create mode 100644 downscaling_test_curv.py create mode 100644 vae.py create mode 100644 verify_scales.py create mode 100644 verify_uv_mapping.py diff --git a/.gitignore b/.gitignore index efc27db..cfe2520 100644 --- a/.gitignore +++ b/.gitignore @@ -142,4 +142,5 @@ models/ models_archive/ temp.jpg corrupt/ -wandb copy/ \ No newline at end of file +wandb copy/ +checkpoints/ diff --git a/auto_encoder.py b/auto_encoder.py index 11427cf..70d76df 100644 --- a/auto_encoder.py +++ b/auto_encoder.py @@ -1,7 +1,6 @@ import os import time import argparse -from cv2 import polarToCart import wandb import random import numpy as np @@ -9,6 +8,7 @@ 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 @@ -16,16 +16,25 @@ 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 +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 +from custom_utils.smpl import SMPL - -os.environ["CUDA_VISIBLE_DEVICES"] = "2" -run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='ae_unposed_1', notes='convnext + stylegan generator') -artifact = wandb.Artifact('code', type='code') -artifact.add_file('auto_encoder.py') -artifact.add_file('stylegan2_ae/layers/mapping_block.py') -run.log_artifact(artifact) +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) @@ -67,8 +76,46 @@ def filter_resolutions_featuremaps(resolutions, featuremaps, res): return filtered_resolutions, filtered_featuremaps -def plot_3d(mesh_gt, mesh_pred, faces): - mesh_pred[:,1] += 2 +# 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 + + +def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color): + mesh_pred_unposed[:,1] += 1 + collision[:,1] += 2 fig = go.Figure(data=[ go.Mesh3d( # 8 vertices of a cube @@ -82,13 +129,24 @@ def plot_3d(mesh_gt, mesh_pred, faces): ), go.Mesh3d( # 8 vertices of a cube - x=mesh_pred[:,0], - y=mesh_pred[:,1], - z=mesh_pred[:,2], + x=mesh_pred_unposed[:,0], + y=mesh_pred_unposed[:,1], + z=mesh_pred_unposed[:,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=collision[:,0], + y=collision[:,1], + z=collision[:,2], + # i, j and k give the vertices of triangles + i = collision_faces[:,0], + j = collision_faces[:,1], + k = collision_faces[:,2], + vertexcolor=collision_color ) ]) @@ -103,31 +161,35 @@ def plot_3d(mesh_gt, mesh_pred, faces): 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__() - resolutions = [4, 8, 16, 32, 64, 128, 256, 512, 1024] - g_featuremaps = [256, 256, 256, 256, 256, 128, 64, 32, 16] - train_resolutions, train_g_featuremaps = filter_resolutions_featuremaps(resolutions, g_featuremaps, 512) - g_params = { - 'z_dim': 256, - 'w_dim': 256, - 'labels_dim': -1, - 'n_mapping': 8, - 'resolutions': train_resolutions, - 'featuremaps': train_g_featuremaps, - } - # test_latent = tf.ones((1, g_params['z_dim']), dtype=tf.float32) - self.decoder = Generator(g_params) - # _ = generator(test_latent) + bottleneck_size = 32 - - self.encoder = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 256, g_params['z_dim']], include_top=False) - self.pooling = tf.keras.layers.GlobalAveragePooling2D() + 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): @@ -135,105 +197,237 @@ class AutoEncoder(tf.keras.Model): x = tf.transpose(x, [0,2,3,1]) x = self.encoder(x, training=training) - x = self.pooling(x) + # 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): - uv_unposed, mesh_unposed, uv_mask, mesh_mask = batch with tf.GradientTape() as tape: - pred = model([uv_unposed, uv_mask]) + pred = model([batch['uv_unposed'], batch['uv_mask']]) + # pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis] mesh_pred = uv2mesh(pred) - mesh_orig = mesh_unposed + # mesh_pred = pred + mesh_orig = batch['mesh_unposed'] - e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask) - c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask) - l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(uv_unposed - pred)), axis=1) * uv_mask) - loss = l1_loss + e_loss + c_loss - metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask) + 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 { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric } + 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): - uv_unposed, mesh_unposed, uv_mask, mesh_mask = batch + pred = model([batch['uv_unposed'], batch['uv_mask']]) - pred = model([uv_unposed, uv_mask]) mesh_pred = uv2mesh(pred) - mesh_orig = mesh_unposed + 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 + } - e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask) - c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask) - l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(uv_unposed - pred)), axis=1) * uv_mask) - loss = l1_loss + e_loss + c_loss - metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask) - return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric } +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()) - - name = 'ae_unposed_1' - dataset_train = DatasetSimple('/home/workspace/data/dataset/stylegan_new/train').cache().batch(8).repeat() - dataset_valid = DatasetSimple('/home/workspace/data/dataset/stylegan_new/valid').cache().batch(8).repeat() + 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) - optimizer = tf.optimizers.Adam() + 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') + # 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) - for key in train_loss.keys(): - with train_summary_writer.as_default(): - tf.summary.scalar(key, train_loss[key], step=step) + 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) - data_valid = batch - # data_valid = iter(dataset_valid).get_next() - # valid_loss = valid_step(data_valid) - - # for key in valid_loss.keys(): - # with valid_summary_writer.as_default(): - # tf.summary.scalar(key, valid_loss[key], step=step) - - if step % 250 == 0: - pred = model([data_valid[0], data_valid[2]]) + 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) - mesh_pred = uv2mesh(pred) - mesh_orig = data_valid[1] - - orig_img = tf.transpose(data_valid[0], [0,2,3,1]) - pred_img = tf.transpose(pred, [0,2,3,1]) - img = tf.concat((orig_img, pred_img), axis=2) - with train_summary_writer.as_default(): - tf.summary.image('images', img, step=step) - - F = np.load("custom_utils/smpl_faces.npy") - garment, faces = maskFacesBatch(mesh_pred.numpy(), F, data_valid[3].numpy().astype(bool)) - gt = [mesh_orig[i].numpy()[data_valid[3][i].numpy().astype(bool), :] for i in range(pred.shape[0])] - - for i in range(pred.shape[0]): - fig = plot_3d(gt[i], garment[i], faces[i]) - wandb.log({"garment_" + str(i): fig}) - print(step) diff --git a/auto_encoder_body.py b/auto_encoder_body.py new file mode 100644 index 0000000..232d624 --- /dev/null +++ b/auto_encoder_body.py @@ -0,0 +1,435 @@ +import os +import time +import argparse +import wandb +import random +import cv2 +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 +from custom_utils.smpl import SMPL + +name='ae_body_5' +def init_wandb(): + os.environ["CUDA_VISIBLE_DEVICES"] = "3" + run = wandb.init(project='vae', 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.\ + two more downscaling than up, so decoder output is resized wto final resolution\ + larger batch_size.\ + flattened bottleneck with 128 latent_dim\ remove global orientation') + artifact = wandb.Artifact('code', type='code') + artifact.add_file('auto_encoder_body.py') + artifact.add_file('losses.py') + artifact.add_file('dataset.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_unposed, faces, collision, collision_faces, collision_color): + mesh_pred_unposed[:,1] += 1 + collision[:,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_unposed[:,0], + y=mesh_pred_unposed[:,1], + z=mesh_pred_unposed[:,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=collision[:,0], + y=collision[:,1], + z=collision[:,2], + # i, j and k give the vertices of triangles + i = collision_faces[:,0], + j = collision_faces[:,1], + k = collision_faces[:,2], + vertexcolor=collision_color + ) + ]) + + 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, latent_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)] + ) + self.flatten = tf.keras.layers.Flatten() + self.dense = tf.keras.layers.Dense(latent_dim) + + def call(self, inputs, training=None): + x = inputs + + x = self.bottleneck(x) + x = self.flatten(x) + x = self.dense(x) + + return x + + +class Decoder(tf.keras.Model): + + def __init__(self, bottleneck_size, latent_dim): + super(Decoder, self).__init__() + + self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size) + self.reshape = tf.keras.layers.Reshape((4, 2, 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 = inputs + + x = self.dense(x) + x = self.reshape(x) + x = self.decoder(x) + + return x + + +class AutoEncoder(tf.keras.Model): + + def __init__(self): + super(AutoEncoder, self).__init__() + + + bottleneck_size = 512 + latent_dim = 128 + + self.encoder = ConvNeXt(depths=[3, 3, 9, 9, 3, 3], dims=[32, 64, 128, 256, 256, bottleneck_size], include_top=False) + self.bottleneck = Bottleneck(bottleneck_size, latent_dim) + self.decoder = Decoder(bottleneck_size, latent_dim) + + + def call(self, inputs, training=None): + x, _ = 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.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5) + x = tf.transpose(x, [0,3,1,2]) + + # x = x * uv_mask[:,:, tf.newaxis] + x = x * uv_mask_body[tf.newaxis,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_body_posed'], batch['uv_mask']]) + # pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis] + + mesh_pred = uv2mesh(pred) + # mesh_pred = pred + mesh_orig = batch['mesh_body_posed'] + + + e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss = 0 # 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_body_posed'] - 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_body_posed'], [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 = 0 # 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_body_posed'], tf.zeros_like(batch['uv_body_posed'])))), 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_body_posed']) + # 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_body_posed'], batch['uv_mask']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_body_posed'] + e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss = 0 #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_body_posed'] - 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_body_posed'], [0,2,3,1]) + 1, max_val=2)) + fft_loss = 0 #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_body_posed'], tf.zeros_like(batch['uv_body_posed'])))), axis=[1,2,3])) + loss = 1e3 * (1 - ms_ssim) + mesh_orig_2 = uv2mesh(batch['uv_body_posed']) + 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_body_posed'], batch['uv_mask']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_body_posed'] + + orig_img = tf.transpose(batch['uv_body_posed'], [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) + garment_unposed, faces = mesh_pred.numpy(), F + # gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])] + gt = [mesh_orig[i].numpy()[:, :] 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])] + garment_posed = garment_unposed + 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_faces = [np.concatenate((F, faces + 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, collision[i], collision_faces[i], collision_color[i]) for i in range(4) } + # 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 } } + + + +def train(): + 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) + + if step % 2500 == 0: + manager.save(checkpoint_number=step) + + + + print(step) + + step += 1 + +if __name__ == '__main__': + init_wandb() + allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + smpl = SMPL() + uv_mask_body = np.load('custom_utils/mask_256x512.npy') + uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2) + + 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=32, 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=32, signature=signature_dynamic) + static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static) + + model = AutoEncoder() + # lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0005, decay_steps=30000, decay_rate=0.1, staircase=False) + optimizer = tf.optimizers.Adam(learning_rate=0.0001) + folder = 'models/ae_logs/' + + ckpt = tf.train.Checkpoint(model=model) + manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=5) + + train() \ No newline at end of file diff --git a/auto_encoder_curv.py b/auto_encoder_curv.py new file mode 100644 index 0000000..f541c19 --- /dev/null +++ b/auto_encoder_curv.py @@ -0,0 +1,379 @@ +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, ConvNeXtReverseLabels +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 +from custom_utils.smpl import SMPL + +name='ae_curvature_6' +testing = False +def init_wandb(): + os.environ["CUDA_VISIBLE_DEVICES"] = "3" + if not testing: + run = wandb.init(project='vae', name=name, notes='convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128') + artifact = wandb.Artifact('code', type='code') + artifact.add_file('auto_encoder_curv.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, latent_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)] + ) + self.flatten = tf.keras.layers.Flatten() + self.dense = tf.keras.layers.Dense(latent_dim) + + def call(self, inputs, training=None): + x = inputs + + x = self.bottleneck(x) + x = self.flatten(x) + x = self.dense(x) + + return x + + +class Decoder(tf.keras.Model): + + def __init__(self, bottleneck_size, latent_dim): + super(Decoder, self).__init__() + + self.dense = tf.keras.layers.Dense(16 * 8 * bottleneck_size) + self.reshape = tf.keras.layers.Reshape((16, 8, bottleneck_size)) + self.decoder = ConvNeXtReverseLabels(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])), nr_of_classes=7) + + def call(self, inputs, training=None): + x = inputs + + x = self.dense(x) + x = self.reshape(x) + x = self.decoder(x) + + return x + + +class AutoEncoder(tf.keras.Model): + + def __init__(self): + super(AutoEncoder, self).__init__() + + + bottleneck_size = 32 + latent_dim = 128 + self.decoder = Decoder(bottleneck_size, latent_dim) + + self.bottleneck = Bottleneck(bottleneck_size, latent_dim) + + self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, bottleneck_size], include_top=False) + + + + 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.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,:,:] + x = x * uv_mask[:,:,:,tf.newaxis] + + return x + + + + +g_mean_iou = tf.keras.metrics.MeanIoU(num_classes=7) +def get_mean_iou(y_true, y_pred): + g_mean_iou.reset_state() + g_mean_iou.update_state(y_true, y_pred) + return g_mean_iou.result() + +# https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py +def dice_dense(prediction, ground_truth, weight_map=None): + """ + Computing mean-class Dice similarity. + :param prediction: last dimension should have ``num_classes`` + :param ground_truth: segmentation ground truth (encoded as a binary matrix) + last dimension should be ``num_classes`` + :param weight_map: + :return: ``1.0 - mean(Dice similarity per class)`` + """ + + if weight_map is not None: + raise NotImplementedError + prediction = tf.cast(prediction, dtype=tf.float32) + ground_truth = tf.cast(ground_truth, dtype=tf.float32) + ground_truth = tf.reshape(ground_truth, prediction.shape) + # computing Dice over the spatial dimensions + reduce_axes = list(range(len(prediction.shape) - 1)) + dice_numerator = 2.0 * tf.reduce_sum( + prediction * ground_truth, axis=reduce_axes) + dice_denominator = \ + tf.reduce_sum(tf.square(prediction), axis=reduce_axes) + \ + tf.reduce_sum(tf.square(ground_truth), axis=reduce_axes) + + epsilon = 0.00001 + + dice_score = (dice_numerator + epsilon) / (dice_denominator + epsilon) + return tf.reduce_mean(dice_score) + + +@tf.function +def train_step(batch): + + with tf.GradientTape() as tape: + uv_curvature = tf.one_hot(batch['uv_curvature'], 7) + + pred = model([uv_curvature, batch['uv_mask']]) + + + ce_uv_loss = scce(batch['uv_curvature'], pred) + ms_ssim = tf.reduce_mean(tf.image.ssim(pred, uv_curvature, max_val=1)) + # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)[:,:,:,tf.newaxis], batch['uv_curvature'][:,:,:,tf.newaxis], max_val=7*2)) + + # loss = 1e3 * (1 - ms_ssim) + loss = ce_uv_loss + + # mesh_pred = uv2mesh(tf.tile(tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)[:,:,:,tf.newaxis], [1,1,1,3]))[:,:,0] + # mesh_orig = tf.one_hot(batch['mesh_curvature'] + tf.cast(batch['mesh_mask'], tf.int32), 7) + # ce_mesh_loss = scce(mesh_orig, mesh_pred) + + mean_iou = get_mean_iou(batch['uv_curvature'], tf.math.argmax(pred, axis=-1)) + dice = dice_dense(pred, uv_curvature) + + gradients = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/dice': dice #'ce_mesh_loss': ce_mesh_loss + } + + + +@tf.function +def valid_step(batch): + uv_curvature = tf.one_hot(batch['uv_curvature'], 7) + + pred = model([uv_curvature, batch['uv_mask']]) + + ce_uv_loss = scce(batch['uv_curvature'], pred) + ms_ssim = tf.reduce_mean(tf.image.ssim(pred, uv_curvature, max_val=1)) + loss = ce_uv_loss + + mean_iou = get_mean_iou(batch['uv_curvature'], tf.math.argmax(pred, axis=-1)) + dice = dice_dense(pred, uv_curvature) + + return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/dice': dice #'ce_mesh_loss': ce_mesh_loss + } + + + +def train(): + 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) + + to_log = { **train_loss, **valid_loss} + if step % 1000 == 0: + uv_curvature = tf.one_hot(data_valid['uv_curvature'], 7) + pred = model([uv_curvature, data_valid['uv_mask']]) + + orig_img = data_valid['uv_curvature'] + pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32) + img = tf.concat((orig_img, pred_img), axis=2).numpy() + imgs = { 'valid/images': [wandb.Image(img[i,:,:,np.newaxis] / 6.0) for i in range(4)] } + to_log = { **to_log, **imgs} + + wandb.log(to_log, step=step) + print(step) + + if step % 2500 == 0: + manager.save(checkpoint_number=step) + + step += 1 + + +def test(): + ckpt.restore(manager.latest_checkpoint) + for batch in dataset_train: + batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) + uv_curvature = tf.one_hot(batch['uv_curvature'], 7) + + pred = model([uv_curvature, batch['uv_mask']]) + orig_img = batch['uv_curvature'] + pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32) + img = tf.concat((orig_img, pred_img), axis=2).numpy() + + ex.imshow(img[0] / 6.0).show() + print() + + + +if __name__ == '__main__': + init_wandb() + allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + smpl = SMPL() + scce = tf.keras.losses.SparseCategoricalCrossentropy() + + 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,) + 'uv_curvature': (512, 256), + 'mesh_curvature': (14475) + } + 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) + + valid_iterator = iter(dataset_valid) + + 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(0.001, decay_steps=10000, decay_rate=0.1, staircase=False) + optimizer = tf.optimizers.Adam(learning_rate=lr_schedule) + folder = 'models/ae_logs/' + + ckpt = tf.train.Checkpoint(model=model) + manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=5) + + if testing: + test() + else: + train() \ No newline at end of file diff --git a/auto_encoder_static.py b/auto_encoder_static.py new file mode 100644 index 0000000..e94eb52 --- /dev/null +++ b/auto_encoder_static.py @@ -0,0 +1,450 @@ +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 +from custom_utils.smpl import SMPL + +name='ae_static_3' +def init_wandb(): + os.environ["CUDA_VISIBLE_DEVICES"] = "2" + run = wandb.init(project='vae', 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.\ + convnext first stage down/upscale by factor fo 2 instead of 4, and 5 stages instead of 4 so bottlneck same size\ + flattened latent dim to 128') + artifact = wandb.Artifact('code', type='code') + artifact.add_file('auto_encoder_static.py') + artifact.add_file('losses.py') + artifact.add_file('dataset.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 + + +def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color): + mesh_pred_unposed[:,1] += 1 + collision[:,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_unposed[:,0], + y=mesh_pred_unposed[:,1], + z=mesh_pred_unposed[:,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=collision[:,0], + y=collision[:,1], + z=collision[:,2], + # i, j and k give the vertices of triangles + i = collision_faces[:,0], + j = collision_faces[:,1], + k = collision_faces[:,2], + vertexcolor=collision_color + ) + ]) + + 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, latent_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)] + ) + self.flatten = tf.keras.layers.Flatten() + self.dense = tf.keras.layers.Dense(latent_dim) + + def call(self, inputs, training=None): + x = inputs + + x = self.bottleneck(x) + x = self.flatten(x) + x = self.dense(x) + + return x + + +class Decoder(tf.keras.Model): + + def __init__(self, bottleneck_size, latent_dim): + super(Decoder, self).__init__() + + self.dense = tf.keras.layers.Dense(8 * 4 * bottleneck_size) + self.reshape = tf.keras.layers.Reshape((8, 4, bottleneck_size)) + self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3, 3])), dims=list(reversed([bottleneck_size, 128, 128, 64, 16]))) + + def call(self, inputs, training=None): + x = inputs + + x = self.dense(x) + x = self.reshape(x) + x = self.decoder(x) + + return x + + +class AutoEncoder(tf.keras.Model): + + def __init__(self): + super(AutoEncoder, self).__init__() + + + bottleneck_size = 32 + latent_dim = 128 + + self.encoder = ConvNeXt(depths=[3, 3, 9, 3, 3], dims=[32, 64, 128, 128, bottleneck_size], include_top=False) + self.bottleneck = Bottleneck(bottleneck_size, latent_dim) + self.decoder = Decoder(bottleneck_size, latent_dim) + + 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_static'], batch['uv_mask']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_static'] + + e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss = 0 #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_static'] - 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_static'], [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_static'], tf.zeros_like(batch['uv_static'])))), 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 = 1e3 * (1 - ms_ssim) + + mesh_orig_2 = uv2mesh(batch['uv_static']) + # 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/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_static'], batch['uv_mask']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_static'] + e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss = 0 + l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - 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_static'], [0,2,3,1]) + 1, max_val=2)) + fft_loss = 0 #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_static'], tf.zeros_like(batch['uv_static'])))), axis=[1,2,3])) + loss = 1e3 * (1 - ms_ssim) + mesh_orig_2 = uv2mesh(batch['uv_static']) + 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_static'], batch['uv_mask']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_static'] + + orig_img = tf.transpose(batch['uv_static'], [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 = gt + garment_posed = mesh_pred + collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))] + collision_faces = [np.concatenate((faces[i], faces[i] + np.sum(mask[i])), 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 } } + + +def train(): + 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 + +if __name__ == '__main__': + init_wandb() + allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + smpl = SMPL() + + + 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() + 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/' + valid_iterator = iter(dataset_valid) + + ckpt = tf.train.Checkpoint(model=model) + manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=5) + + train() \ No newline at end of file diff --git a/convnext/models/convnext_tf.py b/convnext/models/convnext_tf.py index a2ee7c7..67c8d8b 100644 --- a/convnext/models/convnext_tf.py +++ b/convnext/models/convnext_tf.py @@ -1,9 +1,9 @@ +from unittest import skip import numpy as np import tensorflow as tf -from tensorflow.keras.layers import (Conv2D, Dense, DepthwiseConv2D, +from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D, GlobalAveragePooling2D, Layer, - LayerNormalization) - + LayerNormalization, Flatten, Reshape, UpSampling2D) def drop_path(inputs, drop_prob, is_training): # borrowed from https://github.com/rishigami/Swin-Transformer-TF/blob/main/swintransformer/model.py @@ -109,6 +109,299 @@ class ConvNeXt(tf.keras.Model): LayerNormalization(epsilon=1e-6)] ) self.downsample_layers.append(stem) + for i in range(len(depths) - 1): + downsample_layer = tf.keras.Sequential([ + LayerNormalization(epsilon=1e-6), + Conv2D(dims[i+1], kernel_size=2, strides=2, padding='same')] + ) + self.downsample_layers.append(downsample_layer) + + self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(len(depths)): + stage = tf.keras.Sequential( + [Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + if self.include_top: + self.avg = GlobalAveragePooling2D() + self.norm = LayerNormalization(epsilon=1e-6) # final norm layer + self.head = Dense(num_classes) + else: + self.avg = None + self.norm = None + self.head = None + + def forward_features(self, x): + for i in range(len(self.downsample_layers)): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + return x + + def call(self, x): + x = self.forward_features(x) + if self.include_top: + x = self.avg(x) + x = self.norm(x) + x = self.head(x) + return x + + +class ConvNeXtWithSkip(tf.keras.Model): + r""" ConvNeXt + A Tensorflow keras impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + + Args: + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + include_top (bool): whether to add head or just use it as feature extractor. Default: True + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__(self, + depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], + drop_path_rate=0., layer_scale_init_value=1e-6, + ): + super().__init__() + self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers + stem = tf.keras.Sequential([ + Conv2D(dims[0], kernel_size=4, strides=4, padding='same'), + LayerNormalization(epsilon=1e-6)] + ) + self.downsample_layers.append(stem) + for i in range(len(depths) - 1): + downsample_layer = tf.keras.Sequential([ + LayerNormalization(epsilon=1e-6), + Conv2D(dims[i+1], kernel_size=2, strides=2, padding='same')] + ) + self.downsample_layers.append(downsample_layer) + + self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(len(depths)): + stage = tf.keras.Sequential( + [Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + + def call(self, x): + skip_features = [] + for i in range(len(self.downsample_layers)): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + skip_features.append(x) + return x, skip_features + + +class ConvNeXtReverse(tf.keras.Model): + r""" ConvNeXt + A Tensorflow keras impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + + Args: + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + include_top (bool): whether to add head or just use it as feature extractor. Default: True + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__(self, + depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], + drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., + ): + super().__init__() + self.upsample_layers = [] # stem and 3 intermediate downsampling conv layers + stem = tf.keras.Sequential([ + Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'), + # UpSampling2D((4, 4), interpolation='nearest'), + # Conv2D(dims[0], kernel_size=4, strides=1, padding='same'), + # Dense(dims[0]), + LayerNormalization(epsilon=1e-6)] + ) + self.upsample_layers.append(stem) + for i in range(len(depths) - 1): + downsample_layer = tf.keras.Sequential([ + LayerNormalization(epsilon=1e-6), + Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same') + # UpSampling2D((2,2), interpolation='nearest'), + # Conv2D(dims[i+1], kernel_size=2, strides=1, padding='same'), + # Dense(dims[i+1]), + ]) + self.upsample_layers.append(downsample_layer) + + self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(len(depths)): + stage = tf.keras.Sequential( + [Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.last = Dense(3, activation='tanh') + + def call(self, x): + for i in reversed(range(len(self.upsample_layers))): + x = self.upsample_layers[i](x) + x = self.stages[i](x) + x = self.last(x) + return x + + +class ConvNeXtReverseWithSkip(tf.keras.Model): + r""" ConvNeXt + A Tensorflow keras impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + + Args: + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + include_top (bool): whether to add head or just use it as feature extractor. Default: True + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__(self, + depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], + drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., + ): + super().__init__() + assert len(depths) == len(dims) + self.upsample_layers = [] # stem and 3 intermediate downsampling conv layers + stem = tf.keras.Sequential([ + Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'), + LayerNormalization(epsilon=1e-6)] + ) + self.upsample_layers.append(stem) + for i in range(len(depths) - 1): + downsample_layer = tf.keras.Sequential([ + LayerNormalization(epsilon=1e-6), + Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')] + ) + self.upsample_layers.append(downsample_layer) + + self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(len(depths)): + stage = tf.keras.Sequential( + [Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.last = Dense(3, activation='tanh') + + def call(self, x): + x, skip_features = x + for i in reversed(range(len(self.upsample_layers))): + x = tf.concat((x, skip_features[i]), axis=-1) + x = self.upsample_layers[i](x) + x = self.stages[i](x) + x = self.last(x) + return x + + +class ConvNeXtReverseLabels(tf.keras.Model): + r""" ConvNeXt + A Tensorflow keras impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + + Args: + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + include_top (bool): whether to add head or just use it as feature extractor. Default: True + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__(self, + depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], + drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., + nr_of_classes=7): + super().__init__() + self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers + stem = tf.keras.Sequential([ + Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'), + LayerNormalization(epsilon=1e-6)] + ) + self.downsample_layers.append(stem) + for i in range(3): + downsample_layer = tf.keras.Sequential([ + LayerNormalization(epsilon=1e-6), + Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')] + ) + self.downsample_layers.append(downsample_layer) + + self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(4): + stage = tf.keras.Sequential( + [Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.last = Dense(nr_of_classes, activation='softmax') + + def call(self, x): + for i in reversed(range(4)): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + x = self.last(x) + return x + + +class ConvNeXtDiscriminator(tf.keras.Model): + r""" ConvNeXt + A Tensorflow keras impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + + Args: + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + include_top (bool): whether to add head or just use it as feature extractor. Default: True + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__(self, num_classes=1000, + depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], include_top=True, + drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., + ): + super().__init__() + self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers + stem = tf.keras.Sequential([ + Conv2D(dims[0], kernel_size=4, strides=4, padding='same'), + LayerNormalization(epsilon=1e-6)] + ) + self.downsample_layers.append(stem) for i in range(3): downsample_layer = tf.keras.Sequential([ LayerNormalization(epsilon=1e-6), @@ -127,28 +420,21 @@ class ConvNeXt(tf.keras.Model): self.stages.append(stage) cur += depths[i] - if self.include_top: - self.avg = GlobalAveragePooling2D() - self.norm = LayerNormalization(epsilon=1e-6) # final norm layer - self.head = Dense(num_classes) - else: - self.avg = None - self.norm = None - self.head = None + self.flatten = Flatten() + self.last = Dense(1) - def forward_features(self, x): + + def call(self, x): + x = tf.transpose(x, [0,2,3,1]) for i in range(4): x = self.downsample_layers[i](x) x = self.stages[i](x) + x = self.flatten(x) + x = self.last(x) return x - def call(self, x): - x = self.forward_features(x) - if self.include_top: - x = self.avg(x) - x = self.norm(x) - x = self.head(x) - return x + + model_urls = { diff --git a/convunext.py b/convunext.py new file mode 100644 index 0000000..adf7150 --- /dev/null +++ b/convunext.py @@ -0,0 +1,487 @@ +import os +import time +import argparse +import wandb +import random +import numpy as np +import tensorflow as tf +import scipy.interpolate as spi +from custom_utils.custom_utils import UV2Mesh, maskFacesBatch +from tensorboardX import SummaryWriter +import plotly.graph_objects as go +import plotly.express as ex +from plotly.subplots import make_subplots + +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, ConvNeXtReverseWithSkip, ConvNeXtWithSkip +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 +from custom_utils.smpl import SMPL + +name='convunext_7' +os.environ["CUDA_VISIBLE_DEVICES"] = "6" +run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name=name, + notes='basic pyramid. more balanced levels. take ssim (not ms-ssim) loss at each level. higher lr. on on jumpsuits. \ +uv maps filled with nearest enighbour, up/downscaling with lanczos. only take loss at final level') +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) + + +# 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 + + +def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color): + mesh_pred_unposed[:,1] += 1 + collision[:,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_unposed[:,0], + y=mesh_pred_unposed[:,1], + z=mesh_pred_unposed[:,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=collision[:,0], + y=collision[:,1], + z=collision[:,2], + # i, j and k give the vertices of triangles + i = collision_faces[:,0], + j = collision_faces[:,1], + k = collision_faces[:,2], + vertexcolor=collision_color + ) + ]) + + 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 + + +def plot_3d_levels(mesh_levels, faces): + levels = len(mesh_levels) + plot_levels = mesh_levels.copy() #[mesh_levels[i][0] for i in range(levels)] + for i in range(levels): + plot_levels[i][:,1] = plot_levels[i][:,1] + i * 2 + meshes = [go.Mesh3d( + # 8 vertices of a cube + x=mesh[:,0], + y=mesh[:,1], + z=mesh[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + ) for mesh in mesh_levels] + fig = go.Figure(data= meshes) + + fig.update_layout(scene_aspectmode='manual', + scene_aspectratio=dict(x=1, y=levels, z=1)) + fig.update_layout( + scene = dict( + xaxis = dict(nticks=4, range=[-1,1],), + yaxis = dict(nticks=4, range=[-1,levels*2],), + 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 PyramidLevel(tf.keras.Model): + + def __init__(self, depths=[3, 3, 9, 3], dims=[64, 128, 256, 256]): + super(PyramidLevel, self).__init__() + + # reverse_dims = list(reversed(dims[:-1] + [16])) + # reverse_dims = dims[:-1] + [16] + reverse_dims = [16] + dims[:-1] + self.encoder = ConvNeXtWithSkip(depths=depths, dims=dims) + + self.bottleneck = Bottleneck(dims[-1]) + + self.decoder = ConvNeXtReverseWithSkip(depths=depths, dims=reverse_dims) + + def call(self, inputs, training=None): + x = inputs + x, skip_features = self.encoder(x, training=training) + + x = self.bottleneck(x, training=training) + + x = self.decoder([x, skip_features], training=training) + + return x + + + +def downscale(img, uv_mask=None): + + # img = tf.transpose(tf.squeeze(batch['uv_posed']), [1,2,0]).numpy() + # coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + # img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + # img_data = img[img_idx[:,0], img_idx[:,1], :] + + # interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + # img_2 = interpolator(coords) + # res = np.zeros((512,256,3)) + # res[coords[:,0], coords[:,1], :] = img_2 + # res[img_idx[:,0], img_idx[:,1], :] = img_data + + + results = [img] + if uv_mask is not None: + masks = [uv_mask] + for i in range(4): + # print(np.array(img.shape[:2]) / (2 ** (i + 1))) + results.append(tf.image.resize(img, size=np.array(img.shape[1:3]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.LANCZOS5, antialias=True, preserve_aspect_ratio=True)) + if uv_mask is not None: + masks.append(tf.image.resize(uv_mask[:,:,:,tf.newaxis], size=np.array(uv_mask.shape[1:]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, antialias=False)[:,:,:,0]) + if uv_mask is not None: + return results, masks + return results + +def upscale(imgs): + results = [] + for img in imgs: + results.append(tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5)) + return results + + +class Pyramid(tf.keras.Model): + + def __init__(self): + super(Pyramid, self).__init__() + + self.level_1 = PyramidLevel(depths=[27], dims=[512]) + self.level_2 = PyramidLevel(depths=[9, 27], dims=[256, 256]) + self.level_3 = PyramidLevel(depths=[9, 9, 27], dims=[64, 128, 256]) + self.level_4 = PyramidLevel(depths=[3, 9, 27, 3], dims=[32, 64, 128, 256]) + self.level_5 = PyramidLevel(depths=[3, 3, 9, 27, 9], dims=[32, 64, 128, 256, 256]) + + self.levels = [self.level_5, self.level_4, self.level_3, self.level_2, self.level_1] + + + def call(self, inputs, training=None): + x, uv_mask, uv_shaped_body = inputs + + x = tf.transpose(x, [0,2,3,1]) + x_levels = downscale(x) + + output = self.levels[-1](x_levels[-1]) + output_levels = [output] + + for i in reversed(range(len(x_levels)-1)): + level_out = self.levels[i](x_levels[i]) + output = tf.image.resize(output, size=(output.shape[1] * 2, output.shape[2] * 2), method=tf.image.ResizeMethod.LANCZOS5) + level_out + output_levels.append(output) + + x = output + + x = tf.transpose(x, [0,3,1,2]) + # x = x * uv_mask[:,tf.newaxis,:,:] + + return x, output_levels + +class AutoEncoder(tf.keras.Model): + + def __init__(self): + super(AutoEncoder, self).__init__() + + + # bottleneck_size = 256 + + # self.decoder = ConvNeXtReverseWithSkip(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16]))) + + # self.bottleneck = Bottleneck(bottleneck_size) + + # self.encoder = ConvNeXtWithSkip(depths=[3, 3, 9, 3], dims=[64, 128, 256, bottleneck_size]) + + self.level = PyramidLevel(depths=[3, 3, 3, 3], dims=[64, 128]) + + def call(self, inputs, training=None): + x, uv_mask, uv_shaped_body = inputs + + x = tf.transpose(x, [0,2,3,1]) + x = tf.image.resize(x, size=(256, 128)) + # x, skip_features = self.encoder(x, training=training) + + # x = self.bottleneck(x, training=training) + + # x = self.decoder([x, skip_features], training=training) + + x = self.level(x) + x = x + tf.transpose(uv_shaped_body, [0,2,3,1]) + x = tf.transpose(x, [0,3,1,2]) + + # x = x * uv_mask[:,:, tf.newaxis] + # x = x * uv_mask[:,tf.newaxis,:,:] + + return x + + +@tf.function +def train_step(batch): + + with tf.GradientTape() as tape: + + pred, levels_pred = model([tf.concat((batch['uv_static'], batch['uv_body_posed']), axis=1), batch['uv_mask'], batch['uv_shaped_body']]) + # pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis] + + mesh_pred = uv2mesh(pred) + # mesh_pred = pred + mesh_orig = batch['mesh_unposed'] + + # uv_unposed_levels, uv_mask_levels = downscale(tf.transpose(batch['uv_unposed'], [0,2,3,1]), batch['uv_mask']) + ms_ssim_levels = 0 #tf.reduce_mean([tf.reduce_mean(tf.image.ssim((levels_pred[i] + 1) * uv_mask_levels[len(levels_pred) - i - 1][:,:,:,tf.newaxis], (uv_unposed_levels[len(levels_pred) - i - 1] + 1) * uv_mask_levels[len(levels_pred) - i - 1][:,:,:,tf.newaxis], max_val=10)) for i in range(len(levels_pred))]) + + 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) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], max_val=10)) + fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'] * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3])) + # 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) + optimizer.apply_gradients(zip(gradients, model.trainable_variables)) + return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric, 'ms_ssim': ms_ssim, + 'fft_loss': fft_loss, 'euclid_error_reconst': metric_2, + 'euclid_error_posed': euclid_posed, 'ms_ssim_levels': ms_ssim_levels } + + + +@tf.function +def valid_step(batch): + pred, levels_pred = model([tf.concat((batch['uv_static'], batch['uv_body_posed']), axis=1), batch['uv_mask'], batch['uv_shaped_body']]) + + mesh_pred = uv2mesh(pred) + mesh_orig = batch['mesh_unposed'] + # uv_unposed_levels, uv_mask_levels = downscale(tf.transpose(batch['uv_unposed'], [0,2,3,1]), batch['uv_mask']) + ms_ssim_levels = 0#tf.reduce_mean([tf.reduce_mean(tf.image.ssim(levels_pred[i] + 1, uv_unposed_levels[len(levels_pred) - i - 1] + 1, max_val=10)) for i in range(len(levels_pred))]) + 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) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], max_val=10)) + fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred * batch['uv_mask'][:,tf.newaxis,:,:], tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'] * batch['uv_mask'][:,tf.newaxis,:,:], 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 { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric, 'ms_ssim': ms_ssim, + 'fft_loss': fft_loss, 'euclid_error_reconst': metric_2, 'ms_ssim_levels': ms_ssim_levels + } + + +def plot_everything(writer, batch: dict, type: str): + pred, levels = model([tf.concat((batch['uv_static'], batch['uv_body_posed']), axis=1), batch['uv_mask'], batch['uv_shaped_body']]) + + 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) + levels = upscale(levels) + mesh_pred_levels = [uv2mesh(tf.transpose(levels[i], [0,3,1,2])) for i in range(len(levels))] + with writer.as_default(): + tf.summary.image('images', img, step=step, max_outputs=4) + tf.summary.image('levels', tf.concat(levels, axis=2) , step=step, max_outputs=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() + mesh_posed_levels = [smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_levels[i]).numpy() for i in range(len(levels))] + mesh_posed_levels = [mesh_posed_levels[j][0, mask[0], :] for j in range(len(levels))] + 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))] + + + + fig_levels = plot_3d_levels(mesh_posed_levels, faces[0]) + + 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(pred.shape[0]) } + # fig = plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) + wandb.log( { **figs, **{ type + '/levels_garments': fig_levels } } ) + + +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() + model = Pyramid() + # 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(0.001, 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 = batch_to_dict(batch, signature_dynamic) + batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) + + train_loss = train_step(batch) + + for key in train_loss.keys(): + with train_summary_writer.as_default(): + tf.summary.scalar(key, train_loss[key], step=step) + + # data_valid = 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) + + for key in valid_loss.keys(): + with valid_summary_writer.as_default(): + tf.summary.scalar(key, valid_loss[key], step=step) + + if step % 500 == 0: + plot_everything(train_summary_writer, batch, 'train') + plot_everything(valid_summary_writer, data_valid, 'valid') + + # fig = make_subplots(rows=1, cols=5, start_cell="top-left") + + # for i, l in enumerate(levels): + # fig.add_trace(ex.imshow((l[0] + 1) * 128).data[0], row=1, col=i+1) + + + + print(step) + + step += 1 \ No newline at end of file diff --git a/custom_data/jumpsuit_20_avg_uv.npy b/custom_data/jumpsuit_20_avg_uv.npy new file mode 100644 index 0000000000000000000000000000000000000000..9df050f6b35b6885f5346da9bdd2fb4d11e0b597 GIT binary patch literal 1572992 zcmeFZS5y>Dw?Aw`jF?3c6$3#*5hcuY_Z~1|AkPp)Kqab(U_?<-vZw^bhzS+bqo7EX 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zn&O`ydo$~Lni`q)G<(+`_OAw-zG7DV^V6F=*P21o7+x!i|9-&R+&j&rX)DJn{(j;H z9!s-mdWrY0Y7)U#9!rfhHF2z-U~k$%(+xDe#jFCFy7S)BjWn%gZQa9rOD{94iKb@O z)|5ohh4-3f({v3@Ygt=I($ws^NHEL)=dc=hZgdk(o7kJ0y9Uqj-0NnV;{Ax%zl&ov zho;TU$~!tZmdDaunrhjbUS)6km3yrgn&$9YS2xbrKdhyBG~LX(dYN;T$J&}tQ%}}X z8%^(Vt`^XA6X$9TO)v0T&@D90rfCI_tq(X?x6<@Fv(jnW&8*vKn#;L*lC`yk=T5iN zw4GTg+o_ST7|g6xzE(A}zE2K1PvrX}zT<1H=oNg!tah5dWmXGa2bi^orv1$N zkXc_dYca>_OJ==C(-+KohgtiWwUb$&Giy7uK4aD!%-YMWP0Z?G)<$M^GHVKtt;d*^ zPSfMes-bB$v(BKYh*=AJ1jWp%qv=IvZQx$&v#voaYw1mzmU66GjtE9}=jT4m<7=&A zRx9&P*3qpw!CTB)$=CXrS-Y6^3$r?yRmwJiHnUdKw1~avA@--;G*xn} z-e=Zh9IJNrrk%{{WY*)%>R{Hp% 15000 and balancing_coeff <= 1e-3: + # balancing_coeff = 1e-2 + # if step > 20000 and balancing_coeff <= 1e-3: + # balancing_coeff = 1e-2 + # if step > 25000 and balancing_coeff <= 1e-2: + # balancing_coeff = 1e-1 + # if step > 30000 and balancing_coeff <= 1e-1: + # balancing_coeff = 1 + + # gamma = 0.99 * gamma + 0.01 * train_loss['train/reconstr_loss'] + + to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } } + + wandb.log(to_log, step = step) + + print(step) + + step += 1 \ No newline at end of file diff --git a/dataset.py b/dataset.py index bcf57a7..5b88dc1 100644 --- a/dataset.py +++ b/dataset.py @@ -6,7 +6,7 @@ import random import numpy as np import tensorflow as tf -from custom_utils.custom_utils import nest_conditions +from custom_utils.custom_utils import nest_conditions, batch_to_dict @@ -101,7 +101,7 @@ class DatasetIdx(tf.data.Dataset): def __new__(cls, folder): uv_static_folder = os.path.join(folder, 'pose_shape') - frames = os.listdir(uv_static_folder) + frames = sorted(os.listdir(uv_static_folder))#[453:503] frames = [frame.split('.')[0] for frame in frames] # .split('_')[0] random.shuffle(frames) @@ -119,6 +119,8 @@ class DatasetFactory(): for key in signature.keys(): if key == 'id': tf_signature.append(tf.TensorSpec(shape = (), dtype = tf.string)) + elif key == 'uv_curvature' or key == 'mesh_curvature': + tf_signature.append(tf.TensorSpec(shape = signature[key], dtype = tf.int32)) else: tf_signature.append(tf.TensorSpec(shape = signature[key], dtype = tf.float32)) return tf_signature @@ -139,16 +141,27 @@ class DatasetFactory(): item['id'] = id if 'pose_shape' in signature.keys(): pose_shape = np.load(os.path.join(folder, 'pose_shape', id + '_' + frame_nr + '.npy')) + + ############################## + pose_shape[:3] = 0 + ############################## + item['pose_shape'] = pose_shape if 'uv_posed' in signature.keys(): uv_posed = np.load(os.path.join(folder, 'uv_posed', id + '_' + frame_nr + '.npz')); uv_posed = uv_posed['uv_gt']; uv_posed = np.transpose(uv_posed, [2, 0, 1]) item['uv_posed'] = uv_posed + # if 'uv_body_posed' in signature.keys(): + # uv_body_posed = np.load(os.path.join(folder, 'uv_body_posed', id + '_' + frame_nr + '.npz')); uv_body_posed = uv_body_posed['uv_body']; uv_body_posed = np.transpose(uv_body_posed, [2, 0, 1]) + # item['uv_body_posed'] = uv_body_posed + # if 'mesh_body_posed' in signature.keys(): + # mesh_body_posed = np.load(os.path.join(folder, 'mesh_body_posed', id + '_' + frame_nr + '.npy')) + # item['mesh_body_posed'] = mesh_body_posed if 'uv_body_posed' in signature.keys(): - uv_body_posed = np.load(os.path.join(folder, 'uv_body_posed', id + '_' + frame_nr + '.npz')); uv_body_posed = uv_body_posed['uv_body']; uv_body_posed = np.transpose(uv_body_posed, [2, 0, 1]) - item['uv_body_posed'] = uv_body_posed + uv_posed_body_no_orient = np.load(os.path.join(folder, 'uv_posed_body_no_orient', id + '_' + frame_nr + '.npz')); uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']; uv_posed_body_no_orient = np.transpose(uv_posed_body_no_orient, [2, 0, 1]) + item['uv_body_posed'] = uv_posed_body_no_orient if 'mesh_body_posed' in signature.keys(): - mesh_body_posed = np.load(os.path.join(folder, 'mesh_body_posed', id + '_' + frame_nr + '.npy')) - item['mesh_body_posed'] = mesh_body_posed + mesh_posed_body_no_orient = np.load(os.path.join(folder, 'mesh_posed_body_no_orient', id + '_' + frame_nr + '.npz')); mesh_posed_body_no_orient = mesh_posed_body_no_orient['mesh_posed_body_no_orient'] + item['mesh_body_posed'] = mesh_posed_body_no_orient if 'mesh_posed' in signature.keys(): mesh_posed = np.load(os.path.join(folder, 'mesh_posed', id + '_' + frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed'] item['mesh_posed'] = mesh_posed @@ -158,7 +171,12 @@ class DatasetFactory(): if 'uv_unposed' in signature.keys(): 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]) item['uv_unposed'] = uv_unposed - + if 'uv_curvature' in signature.keys(): + uv_curvature = np.load(os.path.join(folder, 'uv_curvature', id + '_' + frame_nr + '.npz')); uv_curvature = uv_curvature['curvature_uv'] + item['uv_curvature'] = uv_curvature.astype(np.int32) + if 'mesh_curvature' in signature.keys(): + mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', id + '_' + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature'] + item['mesh_curvature'] = mesh_curvature.astype(np.int32) # item = { **static_info, **item } as_list = [] @@ -168,6 +186,10 @@ class DatasetFactory(): return as_list + # dataset = dataset_idx.map(lambda i: tf.py_function(func=read_item, + # inp=[i], + # Tout=self._build_tf_signature(signature) + # ), num_parallel_calls=tf.data.AUTOTUNE).cache().batch(batch_size=batch_size, drop_remainder=True).repeat() dataset = dataset_idx.map(lambda i: tf.py_function(func=read_item, inp=[i], Tout=self._build_tf_signature(signature) @@ -186,44 +208,232 @@ class DatasetFactory(): -os.environ["CUDA_VISIBLE_DEVICES"] = "3" if __name__ == '__main__': - from custom_utils.custom_utils import batch_to_dict + os.environ["CUDA_VISIBLE_DEVICES"] = "6" + from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces + import plotly.graph_objects as go + # import plotly.express as ex + # import cmapy as cm + uv2mesh = UV2Mesh() + + F = np.load("custom_utils/smpl_faces.npy") + + + # dataset = DatasetIdx('/home/workspace/data/dataset/stylegan_new/train') + # for d in dataset: + # print(d) 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,), + # 'mesh_body_posed': (14475, 3,), + # 'mesh_posed': (14475, 3,), + 'mesh_unposed': (14475, 3,), + 'uv_unposed': (3, 512, 256,), + # 'uv_curvature': (512, 256), + # 'mesh_curvature': (14475) } signature_static = { #'uv_static': (3, 512, 256,), # 'mesh_static': (14475, 3,), 'mesh_mask': (14475,), - # 'uv_mask': (512, 256,), + 'uv_mask': (512, 256,), # 'uv_shaped_body': (3, 512, 256,), } - dataset = factory.get_dataset(folder='/home/workspace/data/dataset/stylegan_new/train', - batch_size=1024, + dataset = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train', + batch_size=1, signature=signature_dynamic) - static_dataset = StaticDataset(folder='/home/workspace/data/dataset/stylegan_new/train', + static_dataset = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train', signature=signature_static) - @tf.function + + + + # sum_uv = np.zeros((3, 512, 256), dtype=np.float32) + # sum_mask = np.zeros((512, 256), dtype=np.float32) + # for i, batch in enumerate(dataset): + # print(i) + # batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic)) + # sum_uv += batch['uv_unposed'].numpy().sum(axis=0) + # sum_mask += batch['uv_mask'].numpy().sum(axis=0) + + # avg_jumpsuit = sum_uv / sum_mask[np.newaxis, :, :] + # sum_mask[sum_mask == 0] = 1 + + # x = np.transpose(avg_jumpsuit, [1,2,0]) + # x = uv2mesh(avg_jumpsuit[tf.newaxis,:,:,:])[0] + # go.Figure(data=[ + # go.Mesh3d( + # # 8 vertices of a cube + # x=x[:,0], + # y=x[:,1], + # z=x[:,2], + # # i, j and k give the vertices of triangles + # i = F[:,0], + # j = F[:,1], + # k = F[:,2] + # )]).show() + + # np.save('custom_data/jumpsuit_20_avg_uv.npy', np.transpose(avg_jumpsuit, [1,2,0])) + + # print() + + + + + + uv_res = 256 + + def m2uv_array(m2uv): + result = np.zeros((14475, 2), dtype=np.int32) + for k, v in m2uv.items(): + if len(v) == 2: + result[k] = list(v) + elif len(v) == 1: + x = v.pop() + result[k] = [x, x] + else: + assert False + + return result + + def unique_uv_map_mask(uv_map_discreet): + mask = np.zeros((uv_map_discreet.shape[0]), dtype=bool) + temp = np.zeros((uv_res * 2, uv_res)) + for p in uv_map_discreet: + temp[p[0], p[1]] += 1 + # temp = temp == 1 + for i, p in enumerate(uv_map_discreet): + if temp[p[0], p[1]] == 1: + mask[i] = True + elif temp[p[0], p[1]] == 2: + mask[i] = True + temp[p[0], p[1]] = 0 + return mask + + def uv_to_mesh(uv, m2uv): + res = uv.shape[0] + mesh = np.zeros((14475, 3)) + uv_pixels = uv_map_discreet[m2uv] + # counter = np.ones((14475)) + # counter[m2uv[:, 0] == m2uv[:, 1]] = 2 + mesh = np.mean(uv[uv_pixels[:, :, 0], uv_pixels[:, :, 1], :], axis=1) # / counter[:, np.newaxis] + return mesh + + + uv_map = np.load('custom_utils/stretched_uv_map_3.npy') + m2uv = np.load('custom_utils/m2uv.npy', allow_pickle=True).item() + m2uv = m2uv_array(m2uv) + uv_mask = np.load('custom_utils/mask_256x512.npy') + curvature_label_type = '6_25' + + uv_res = 256 + uv_mask = (cv2.resize(uv_mask * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis] + uv_mask = cv2.dilate(uv_mask.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)[:, :, np.newaxis] + + uv_map_discreet = np.floor(uv_map * uv_res).astype(np.int32) + uv_map_mask = unique_uv_map_mask(uv_map_discreet) + uv_counter = np.zeros((uv_map.shape[0])) + uv_counter[m2uv[:, 0]] = 1 + uv_counter[m2uv[:, 1]] += 1 + uv_counter = uv_counter[uv_map_mask] + + + + for i, batch in enumerate(dataset): + print(i) + batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic)) + + uv_unposed = np.squeeze(batch['uv_unposed'].numpy()) + mesh_unposed = np.squeeze(batch['mesh_unposed'].numpy()) + mask = np.squeeze(batch['mesh_mask'].numpy()).astype(bool) + + + values = mesh_unposed + uv_values = np.zeros((uv_map.shape[0], 3)) + uv_values[m2uv[:, 0]] = values + uv_values[m2uv[:, 1]] += values + uv_counter = np.zeros((uv_map.shape[0])) + uv_counter[m2uv[:, 0]] = 1 + uv_counter[m2uv[:, 1]] += 1 + uv_values /= uv_counter[:, np.newaxis] + x = uv_values + # verts = np.concatenate((verts, np.zeros((15068, 1))), axis=-1) + im = ((x - x.min()) / (x.max() - x.min())) * 255 + + + x = np.transpose(uv_unposed, [1,2,0]) + x = uv2mesh(uv_unposed[tf.newaxis,:,:,:])[0].numpy()[mask, :] + _, faces = _maskFaces(np.zeros((14475, 3), dtype=np.float32), F, mask) + go.Figure(data=[ + go.Mesh3d( + # 8 vertices of a cube + x=x[:,0], + y=x[:,1], + z=x[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + )]).show() + + + print() + + + + + # @tf.function def euclid_error(mask, gt, pred): - return tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mask[:,:,tf.newaxis] * (gt - pred) ** 2, axis=-1))) / tf.reduce_sum(mask) + # return tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mask[:,:,tf.newaxis] * (gt - pred) ** 2, axis=-1)), axis=-1) / tf.reduce_sum(mask, axis=-1) + return tf.math.sqrt(tf.reduce_sum(mask[:,:,tf.newaxis] * (gt - pred) ** 2, axis=-1)) all_error = [] for batch in dataset: batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic)) - err = euclid_error(batch['mesh_mask'], batch['mesh_posed'], batch['mesh_body_posed']) - all_error.append(err.numpy()) + mesh_reconstruct = uv2mesh(batch['uv_posed']) + err = euclid_error(batch['mesh_mask'], batch['mesh_posed'], uv2mesh(batch['uv_posed'])) + + i = 296 + err_normed = (err[i].numpy() - err[i].numpy().min()) / (err[i].numpy().max() - err[i].numpy().min()) + colors = np.array([cm.color('jet', 1 - i) for i in err_normed]) + + _, faces = _maskFaces(batch['mesh_posed'][i].numpy(), F, batch['mesh_mask'][i].numpy().astype(bool)) + x = batch['mesh_posed'][i].numpy()[batch['mesh_mask'][i].numpy().astype(bool), :] + y = mesh_reconstruct[i].numpy()[batch['mesh_mask'][i].numpy().astype(bool), :] + colors = colors[batch['mesh_mask'][i].numpy().astype(bool), :] + fig = go.Figure(data=[ + go.Mesh3d( + # 8 vertices of a cube + x=x[:,0], + y=x[:,1], + z=x[:,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=y[:,0], + y=y[:,1], + z=y[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2], + )]) + fig.show() + + fig = ex.scatter_3d(x=x[:,0], y=x[:,1], z=x[:,2]) + fig.update_traces(marker={'color': colors, 'size': 5}) + fig.show() + + all_error.append(err.numpy().sum()) # b = nest_conditions(static_dataset.expand_batch(batch_to_dict(d, signature_dynamic))) print(len(all_error)) @@ -232,25 +442,7 @@ if __name__ == '__main__': from losses import collision_loss as collision_penalty from losses import collision_penalty as collision_penalty_2 - from custom_utils.custom_utils import UV2Mesh - import matplotlib.pyplot as plt - def plot_3d(x, y=None): - fig = plt.figure(figsize=(15, 15)) - ax = fig.add_subplot(111, projection='3d') - ax.set_xlim3d(-1, 1) - ax.set_ylim3d(-1, 1) - ax.set_zlim3d(-1, 1) - ax.set_xlabel("X") - ax.set_ylabel("Y") - ax.set_zlabel("Z") - ax.grid(False) - ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=5) - if y is not None: - ax.scatter(y[:, 0], y[:, 1], y[:, 2], s=5) - plt.savefig('temp.jpg') - - uv2mesh = UV2Mesh() for d in dataset: d diff --git a/docker/Dockerfile b/docker/Dockerfile index a552b72..b11fcb0 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:2.9.0-gpu RUN apt-get update RUN apt-get upgrade -y RUN apt-get install git ffmpeg libsm6 libxext6 libopenexr-dev zip unzip htop nano -y -RUN pip install numpy matplotlib opencv-python wandb OpenEXR tensorflow-graphics scipy plotly pandas tensorboardX opencv-python +RUN pip install numpy matplotlib opencv-python wandb OpenEXR tensorflow-graphics scipy plotly pandas tensorboardX opencv-python cmapy WORKDIR /home/workspace/data diff --git a/docker/requierements.txt b/docker/requierements.txt index cf19a86..64e5458 100644 --- a/docker/requierements.txt +++ b/docker/requierements.txt @@ -8,4 +8,5 @@ scipy plotly pandas tensorboardX -opencv-python \ No newline at end of file +opencv-python +cmapy \ No newline at end of file diff --git a/downscaling_test.py b/downscaling_test.py new file mode 100644 index 0000000..163a480 --- /dev/null +++ b/downscaling_test.py @@ -0,0 +1,259 @@ +import os +import time +import argparse +import wandb +import random +import tqdm +import scipy as sp +import scipy.interpolate as spi +import numpy as np +import tensorflow as tf +from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces +import plotly.graph_objects as go +import plotly.express as ex +from plotly.subplots import make_subplots + +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 +from custom_utils.smpl import SMPL + + +os.environ["CUDA_VISIBLE_DEVICES"] = "6" + +uv2mesh = UV2Mesh(res=256) + + +def downscale_1(img): + results = [img] + for i in range(5): + results.append(tf.image.resize(img, size=np.array(img.shape[:2]) / (2 * (i + 1)), method=tf.image.ResizeMethod.LANCZOS5)) + return results + +def downscale_2(img): + results = [img] + for i in range(5): + # print(np.array(img.shape[:2]) / (2 ** (i + 1))) + results.append(tf.image.resize(img, size=np.array(img.shape[:2]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, antialias=True, preserve_aspect_ratio=True)) + return results + +def upscale_1(imgs): + results = [] + for img in imgs: + results.append(tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5)) + return results + + +def optimize(img, gt): + lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(1.0, decay_steps=40000, decay_rate=0.1, staircase=False) + optimizer = tf.keras.optimizers.SGD(learning_rate=lr_schedule) + img = tf.Variable(img) + + @tf.function + def error(): + pred = tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5) + loss = 1 - tf.image.ssim_multiscale(pred + 1, gt + 1, max_val=2) + return loss + + for i in range(1000): + optimizer.minimize(error, [img]) + + if i % 100 == 0: + pred = tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5) + loss = 1 - tf.image.ssim_multiscale(pred + 1, gt + 1, max_val=2) + print('it: ' + str(i) + ' loss: ' + str(loss.numpy())) + + return img + + + +def fill(img, mask): + coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0)) + img_data = img[img_idx[:,0], img_idx[:,1], :] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_2 = interpolator(coords) + res = np.zeros((512,256,3), dtype=np.float32) + res[coords[:,0], coords[:,1], :] = img_2 + res[img_idx[:,0], img_idx[:,1], :] = img_data + + return res + + +def fill_uv_maps(folder, type): + os.makedirs(os.path.join(folder, type + '_filled'), exist_ok=True) + mask = np.load('custom_utils/mask_256x512.npy') + + frames = os.listdir(os.path.join(folder, type)) + + for frame in tqdm.tqdm(frames): + id, _ = frame.split('_') + # mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); mask = mask['uv_mask'] + img = np.load(os.path.join(folder, type, frame)); img = img['uv_body'] + filled = fill(img, mask) + np.savez_compressed(os.path.join(folder, type + '_filled', frame), uv_body_posed = filled) + + + +if __name__ == '__main__': + # fill_uv_maps(folder='/home/workspace/data/dataset/stylegan_jumpsuit/train', type='uv_body_posed') + + # pass + # exit(0) + + allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + smpl = SMPL() + + + 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/stylegan_new/train', + batch_size=1, + signature=signature_dynamic) + static_dataset_train = StaticDataset(folder='/home/workspace/local-data/stylegan_new/train', + signature=signature_static) + + + step = 0 + for batch in dataset_train: + # batch = batch_to_dict(batch, signature_dynamic) + batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) + + img = tf.transpose(tf.squeeze(batch['uv_posed']), [1,2,0]).numpy() + coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + img_data = img[img_idx[:,0], img_idx[:,1], :] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_2 = interpolator(coords) + res = np.zeros((512,256,3)) + res[coords[:,0], coords[:,1], :] = img_2 + res[img_idx[:,0], img_idx[:,1], :] = img_data + + img_2 = res + + down_1 = downscale_1(img) + down_2 = downscale_1(img_2) + up_1 = upscale_1(down_1) + up_2 = upscale_1(down_2) + + mask = batch['mesh_mask'].numpy().astype(bool)[0] + mesh_2 = [uv2mesh(tf.transpose(up_2[i], [2,0,1])[tf.newaxis,:,:,:])[0] for i in range(len(up_2))] + mesh_1 = [uv2mesh(tf.transpose(up_1[i], [2,0,1])[tf.newaxis,:,:,:])[0] for i in range(len(up_1))] + F = np.load("custom_utils/smpl_faces.npy") + _, faces = _maskFaces(np.ones((14475, 3)), F, mask) + + # mesh_downscaled = mesh_2[-1].numpy()[mask, :] + # uv_optimized = optimize(down_2[-1], tf.transpose(batch['uv_posed'][0], [1,2,0])) + # uv_optimized = tf.image.resize(uv_optimized, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5) + # mesh_optimized = uv2mesh(tf.transpose(uv_optimized, [2,0,1])[tf.newaxis,:,:,:])[0].numpy()[mask, :] + + # go.Figure(data=[ + # go.Mesh3d( + # # 8 vertices of a cube + # x=mesh_downscaled[:,0], + # y=mesh_downscaled[:,1], + # z=mesh_downscaled[:,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_optimized[:,0], + # y=mesh_optimized[:,1], + # z=mesh_optimized[:,2], + # # i, j and k give the vertices of triangles + # i = faces[:,0], + # j = faces[:,1], + # k = faces[:,2], + # ) + # ]).show() + + + # fig = make_subplots(rows=2, cols=6, start_cell="top-left") + + # for i, d1 in enumerate(down_1): + # fig.add_trace(ex.imshow((d1 + 1) * 128).data[0], row=1, col=i+1) + + # for i, u1 in enumerate(up_1): + # fig.add_trace(ex.imshow((u1 + 1) * 128).data[0], row=2, col=i+1) + + # fig.show() + + fig = make_subplots(rows=2, cols=6, start_cell="top-left") + + for i, d2 in enumerate(down_2): + fig.add_trace(ex.imshow((d2 + 1) * 128).data[0], row=1, col=i+1) + + for i, u2 in enumerate(up_2): + fig.add_trace(ex.imshow((u2 + 1) * 128).data[0], row=2, col=i+1) + + fig.show() + + fig = make_subplots(rows=2, cols=6, start_cell="top-left", specs = [ [{ 'type': 'xy' } for _ in range(6)], [{ 'type': 'scene' } for _ in range(6)] ]) + + for i, u2 in enumerate(up_2): + fig.add_trace(ex.imshow((u2 + 1) * 128).data[0], row=1, col=i+1) + + # for i, m1 in enumerate(mesh_1): + # m1 = m1.numpy()[mask, :] + # fig.add_trace(go.Mesh3d( + # # 8 vertices of a cube + # x=m1[:,0], + # y=m1[:,1], + # z=m1[:,2], + # # i, j and k give the vertices of triangles + # i = faces[:,0], + # j = faces[:,1], + # k = faces[:,2] + # ), + # row=1, col=i+1) + + for i, m2 in enumerate(mesh_2): + m2 = m2.numpy()[mask, :] + fig.add_trace(go.Mesh3d( + # 8 vertices of a cube + x=m2[:,0], + y=m2[:,1], + z=m2[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + ), + row=2, col=i+1) + + fig.show() + + print() \ No newline at end of file diff --git a/downscaling_test_curv.py b/downscaling_test_curv.py new file mode 100644 index 0000000..0a58101 --- /dev/null +++ b/downscaling_test_curv.py @@ -0,0 +1,227 @@ +import os +import time +import argparse +import wandb +import random +import tqdm +import scipy as sp +import cmapy as cm +import scipy.interpolate as spi +import numpy as np +import tensorflow as tf +from custom_utils.custom_utils import UV2MeshLabel, maskFacesBatch, _maskFaces +import plotly.graph_objects as go +import plotly.express as ex +from plotly.subplots import make_subplots + +from utils import str_to_bool +from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing +# 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 +from custom_utils.smpl import SMPL + + +os.environ["CUDA_VISIBLE_DEVICES"] = "1" + +uv2mesh = UV2MeshLabel(res=256) + + +def downscale_1(img): + results = [img] + for i in range(5): + results.append(tf.image.resize(img, size=np.array(img.shape[:2]) / (2 * (i + 1)), method=tf.image.ResizeMethod.LANCZOS5)) + return results + +def downscale_2(img): + results = [img] + for i in range(5): + # print(np.array(img.shape[:2]) / (2 ** (i + 1))) + results.append(tf.image.resize(img, size=np.array(img.shape[:2]) / (2 ** (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, antialias=True, preserve_aspect_ratio=True)) + return results + +def upscale_1(imgs): + results = [] + for img in imgs: + results.append(tf.image.resize(img, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5)) + return results + + +def fill(img, mask): + coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0)) + img_data = img[img_idx[:,0], img_idx[:,1], :] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_2 = interpolator(coords) + res = np.zeros((512,256,3), dtype=np.float32) + res[coords[:,0], coords[:,1], :] = img_2 + res[img_idx[:,0], img_idx[:,1], :] = img_data + + return res + + +def fill_uv_maps(folder, type): + os.makedirs(os.path.join(folder, type + '_filled'), exist_ok=True) + mask = np.load('custom_utils/mask_256x512.npy') + + frames = os.listdir(os.path.join(folder, type)) + + for frame in tqdm.tqdm(frames): + id, _ = frame.split('_') + # mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); mask = mask['uv_mask'] + img = np.load(os.path.join(folder, type, frame)); img = img['uv_body'] + filled = fill(img, mask) + np.savez_compressed(os.path.join(folder, type + '_filled', frame), uv_body_posed = filled) + + + +if __name__ == '__main__': + # fill_uv_maps(folder='/home/workspace/data/dataset/stylegan_jumpsuit/train', type='uv_body_posed') + + # pass + # exit(0) + + allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + smpl = SMPL() + + + 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,) + 'uv_curvature': (512, 256), + 'mesh_curvature': (14475) + } + 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/stylegan_new/train', + batch_size=1, + signature=signature_dynamic) + static_dataset_train = StaticDataset(folder='/home/workspace/local-data/stylegan_new/train', + signature=signature_static) + + + step = 0 + for batch in dataset_train: + # batch = batch_to_dict(batch, signature_dynamic) + batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) + + img = tf.squeeze(batch['uv_curvature']).numpy() + coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + img_data = img[img_idx[:,0], img_idx[:,1]] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_2 = interpolator(coords) + res = np.zeros((512,256)) + res[coords[:,0], coords[:,1]] = img_2 + res[img_idx[:,0], img_idx[:,1]] = img_data + + img_2 = res + + i = 0 + down_curv = tf.image.resize(img_2[:,:,tf.newaxis], size=np.array(img_2.shape[:2]) / (2 * (i + 1)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) + down_curv = tf.image.resize(down_curv, size=(512, 256), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)[:,:,0] + + + + img_posed = tf.transpose(tf.squeeze(batch['uv_posed']), [1,2,0]).numpy() + coords = np.transpose(np.stack(np.nonzero((1 - batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + img_idx = np.transpose(np.stack(np.nonzero((batch['uv_mask'][0]).numpy().astype(bool))), (1,0)) + img_data = img_posed[img_idx[:,0], img_idx[:,1], :] + + interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) + img_posed_2 = interpolator(coords) + res = np.zeros((512,256,3)) + res[coords[:,0], coords[:,1], :] = img_posed_2 + res[img_idx[:,0], img_idx[:,1], :] = img_data + + img_posed_2 = res + + mesh_posed = uv2mesh(tf.transpose(img_posed_2[i], [2,0,1])[tf.newaxis,:,:,:])[0] + + + + mask = batch['mesh_mask'].numpy().astype(bool)[0] + mesh_1 = uv2mesh(img_2[tf.newaxis,:,:])[0].numpy()[mask] + mesh_2 = uv2mesh(down_curv[tf.newaxis,:,:])[0].numpy()[mask] + F = np.load("custom_utils/smpl_faces.npy") + _, faces = _maskFaces(np.ones((14475, 3)), F, mask) + + fig = make_subplots(rows=1, cols=2, start_cell="top-left") + + fig.add_trace(ex.imshow((img_2) / 6).data[0], row=1, col=1) + fig.add_trace(ex.imshow((down_curv ) / 6).data[0], row=1, col=2) + + fig.show() + + mesh = tf.squeeze(batch['mesh_posed']).numpy()[mask] + colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)]) + v_color_1 = np.zeros((mesh.shape[0], 3)) + v_color_2 = np.zeros((mesh.shape[0], 3)) + for i in range(6+1): + v_color_1[mesh_1 == i, :] = colors[i] + v_color_2[mesh_2 == i, :] = colors[i] + + mesh_copy = mesh.copy() + mesh_copy[:,1] += 1 + fig = go.Figure(data=[ + go.Mesh3d( + # 8 vertices of a cube + x=mesh[:,0], + y=mesh[:,1], + z=mesh[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2], + vertexcolor=v_color_1, + text='original' + ), + go.Mesh3d( + # 8 vertices of a cube + x=mesh_copy[:,0], + y=mesh_copy[:,1], + z=mesh_copy[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2], + vertexcolor=v_color_2, + text='rescaled' + ) + ]) + + 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],),) + ) + + fig.show() + + + print() \ No newline at end of file diff --git a/load_models.py b/load_models.py index 19b4f4f..3d675cc 100644 --- a/load_models.py +++ b/load_models.py @@ -2,10 +2,13 @@ import tensorflow as tf def load_generator(g_params=None, is_g_clone=False, ckpt_dir=None, custom_cuda=True): - if custom_cuda: - from stylegan2.generator import Generator - else: - from stylegan2_ref.generator import Generator + # if custom_cuda: + # from stylegan2.generator import Generator + # else: + # from stylegan2_ref.generator import Generator + from stylegan2.generator import ConvNeXtReverseConditional as Generator + + if g_params is None: g_params = { @@ -23,7 +26,7 @@ def load_generator(g_params=None, is_g_clone=False, ckpt_dir=None, custom_cuda=T test_conditions = { 'pose_shape': tf.ones((1, 83), dtype=tf.float32), 'uv_static': tf.ones((1, 3, 512, 256), dtype=tf.float32), 'uv_mask': tf.ones((1, 512, 256), dtype=tf.float32), 'uv_shaped_body': tf.ones((1, 3, 512, 256), dtype=tf.float32) } # build generator model - generator = Generator(g_params) + generator = Generator() _ = generator([test_latent, test_conditions]) if ckpt_dir is not None: @@ -44,7 +47,8 @@ def load_discriminator(d_params=None, ckpt_dir=None, custom_cuda=True): # else: # from stylegan2_ref.discriminator import Discriminator - from custom_utils.graph_utils import GNNDiscriminator as Discriminator + # from custom_utils.graph_utils import GNNDiscriminator as Discriminator + from convnext.models.convnext_tf import ConvNeXtDiscriminator as Discriminator res = d_params['resolutions'][-1] @@ -52,7 +56,8 @@ def load_discriminator(d_params=None, ckpt_dir=None, custom_cuda=True): test_images = tf.ones((batch_size, 3, res, int(res/2)), dtype=tf.float32) # build discriminator model - discriminator = Discriminator(batch_size, 32) + # discriminator = Discriminator(batch_size, 32) + discriminator = Discriminator(depths=[3, 3, 27, 3], dims=[96, 192, 256, 8], include_top=False) _ = discriminator(test_images) if ckpt_dir is not None: diff --git a/losses.py b/losses.py index 8d84f7c..020fc2f 100644 --- a/losses.py +++ b/losses.py @@ -6,6 +6,11 @@ import scipy.io as sio from scipy.spatial import cKDTree from tensorflow_graphics.geometry.representation.mesh.normals import vertex_normals +from tensorflow_graphics.geometry.convolution.graph_convolution import edge_convolution_template + +from custom_utils.custom_utils import maskFacesBatch, _maskFaces +from custom_utils.graph_utils import sparse_to_tensor + def d_logistic(real_images, generator, discriminator, z_dim, conditions): batch_size = tf.shape(real_images)[0] @@ -202,6 +207,57 @@ def uv_mask_loss(pred, uv_mask): return invalid_count / total_count + + +def merge_verts(Vs, batchDim): + V = tf.concat(tf.unstack(Vs, num=batchDim), axis=0) + return V + + +def _bend_loss_single(x, F, L, mask): + verts = tf.boolean_mask(x, mask, axis=0) + VN = vertex_normals(verts, F) + bend = edge_convolution_template( + VN, L, sizes=None, + edge_function=lambda x,y: (x - y)**2, + reduction='weighted', + edge_function_kwargs={} + ) + loss = tf.reduce_sum(bend) + return loss + +def bend_loss(x, F, L, mask): + # x: predicted outfits + # F: faces + # L: laplacian + loss = tf.stack([_bend_loss_single(x[i], F[i], L[i], mask[i]) for i in range(x.shape[0])]) + return loss + + +# def bend_loss(x, F, L, mask): +# # x: predicted outfits +# # F: faces +# # L: laplacian +# # x = x * mask[:,:,tf.newaxis] +# batch_size = x.shape[0] +# verts, faces = maskFacesBatch(x, F, mask) +# nr_valid_verts = [tf.reduce_sum(mask[i]) for i in range(batch_size)] +# x = tf.concat(verts, axis=0) +# F = tf.concat([faces[i] + tf.reduce_sum(nr_valid_verts[:i]) for i in range(1, batch_size)]) +# F = tf.concat((faces[0], F), axis=0) +# VN = vertex_normals(x, F) +# bend = edge_convolution_template( +# VN, L, sizes=None, +# edge_function=lambda x,y: (x - y)**2, +# reduction='weighted', +# edge_function_kwargs={} +# ) +# loss = tf.reduce_sum(bend, axis=[1]) +# loss = tf.reshape(loss, (batch_size, 14475)) +# loss = tf.reduce_mean(loss, axis=1) +# return loss + + if __name__ == '__main__': V = tf.ones((4, 14475, 3)) B = tf.zeros((4, 14475, 3)) diff --git a/stylegan2/generator.py b/stylegan2/generator.py index 96275b7..9dcb9b8 100644 --- a/stylegan2/generator.py +++ b/stylegan2/generator.py @@ -1,10 +1,19 @@ import tensorflow as tf +import numpy as np from stylegan2.utils import lerp from stylegan2.layers.mapping_block import Mapping from stylegan2.layers.synthesis_block import Synthesis +from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D, + GlobalAveragePooling2D, Layer, + LayerNormalization, Flatten, Reshape) +# from stylegan2.layers.mapping_block import Mapping +from stylegan2.layers.embedding import PoseShapeEmbedding, GarmentEmbedding +from convnext.models.convnext_tf import Block, DropPath + + class Generator(tf.keras.Model): def __init__(self, g_params, **kwargs): super(Generator, self).__init__(**kwargs) @@ -115,3 +124,86 @@ class Generator(tf.keras.Model): def compute_output_shape(self, input_shape): return input_shape[0][0], 3, self.resolutions[-1], self.resolutions[-1] + + + + +class ConvNeXtReverseConditional(tf.keras.Model): + r""" ConvNeXt + A Tensorflow keras impl of : `A ConvNet for the 2020s` - + https://arxiv.org/pdf/2201.03545.pdf + + Args: + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + include_top (bool): whether to add head or just use it as feature extractor. Default: True + drop_path_rate (float): Stochastic depth rate. Default: 0. + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__(self, + depths=[3, 3, 9, 3], dims=[64, 64, 32, 4], + drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., + ): + super().__init__() + + self.pose_shape_embedding = PoseShapeEmbedding() + self.garment_embedding = GarmentEmbedding((128 * 3) - (216 + 11)) + + self.reshape = Reshape((16, 8, 4)) + + self.upsample_layers = [] # stem and 3 intermediate downsampling conv layers + stem = tf.keras.Sequential([ + Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'), + LayerNormalization(epsilon=1e-6)] + ) + self.upsample_layers.append(stem) + for i in range(3): + downsample_layer = tf.keras.Sequential([ + LayerNormalization(epsilon=1e-6), + Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')] + ) + self.upsample_layers.append(downsample_layer) + + self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(4): + stage = tf.keras.Sequential( + [Block(dim=dims[i], drop_path=dp_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.last = Dense(3, activation='tanh') + + def call(self, inputs, ret_w_broadcasted=False, truncation_psi=1.0, truncation_cutoff=None,): + latents, conditions = inputs + + pose_shape_latent = self.pose_shape_embedding([conditions['pose_shape'][:, :72], conditions['pose_shape'][:, 72:]]) + garment_latent = self.garment_embedding(conditions['uv_static']) + x = tf.concat([latents, pose_shape_latent, garment_latent], axis=1) + x = self.reshape(x) + + for i in reversed(range(4)): + x = self.upsample_layers[i](x) + x = self.stages[i](x) + x = self.last(x) + + x = tf.transpose(x, [0,3,1,2]) + x = x + conditions['uv_shaped_body'] + x = x * conditions['uv_mask'][:,tf.newaxis,:,:] + + return x + + def set_as_moving_average_of(self, src_net): + return + + def update_moving_average_of_w(self, w_broadcasted): + return + + def style_mixing_regularization(self, latents1, conditions, w_broadcasted1): + return None \ No newline at end of file diff --git a/stylegan2/layers/embedding.py b/stylegan2/layers/embedding.py index 2242f93..2ad461d 100644 --- a/stylegan2/layers/embedding.py +++ b/stylegan2/layers/embedding.py @@ -91,7 +91,7 @@ class GarmentEmbedding(tf.keras.layers.Layer): # TODO: channels_first? - self.encoder = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 256, self.embed_dim], include_top=False) + self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, self.embed_dim], include_top=False) # self.encoder(tf.keras.Input(shape=(*input_shape, 3))) self.pooling = tf.keras.layers.GlobalAveragePooling2D() diff --git a/train.py b/train.py index c907c3b..fdee5c5 100644 --- a/train.py +++ b/train.py @@ -17,8 +17,8 @@ from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_l from losses import collision_loss as collision_penalty from custom_utils.smpl import SMPL -os.environ["CUDA_VISIBLE_DEVICES"] = "0" -run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='run_35', notes='learning offsets, all losses from the start, smaller generator (from 128), slower learning rate for generator, add a fading out l1 loss with gt, add tanh activation for last layer, higher weights for the extra losses, same learning rate again. added extra metrics, lower discr. features too. use reduce_sum for losses divided by sum of mask. learn unposed version then do skinning. lowered collsion and edge coeff. learns offsets over shaped body. fixed unposed uv map conversion. gnn discriminator. reworked data loader. per garment sum of errors. per_image_standardization for garment tempalte input') +# os.environ["CUDA_VISIBLE_DEVICES"] = "4" +run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='run_37_2', notes='learning offsets, all losses from the start, smaller generator (from 128), slower learning rate for generator, add a fading out l1 loss with gt, add tanh activation for last layer, higher weights for the extra losses, same learning rate again. added extra metrics, lower discr. features too. use reduce_sum for losses divided by sum of mask. learn unposed version then do skinning. lowered collsion and edge coeff. learns offsets over shaped body. fixed unposed uv map conversion. . reworked data loader. per garment sum of errors. per_image_standardization for garment tempalte input. convnext discriminator, convnext generator. higher starting learning rate') artifact = wandb.Artifact('code', type='code') artifact.add_file('train.py') artifact.add_file('losses.py') @@ -53,7 +53,7 @@ class Trainer(object): self.train_res = t_params['train_res'] self.print_step = 100 self.save_step = 10000 - self.image_summary_step = 1000 + self.image_summary_step = 500 self.unsupervised_coeff = 1 self.gt_loss_coeff = 1 self.reached_max_steps = False @@ -490,8 +490,8 @@ class Trainer(object): test_z, test_conditions = inputs # run networks - fake_images_05 = self.g_clone([test_z, test_conditions], truncation_psi=0.5, training=False) - fake_images_07 = self.g_clone([test_z, test_conditions], truncation_psi=0.7, training=False) + fake_images_05 = self.generator([test_z, test_conditions], truncation_psi=0.5, training=False) + fake_images_07 = self.generator([test_z, test_conditions], truncation_psi=0.7, training=False) # merge on batch dimension: [n_samples, 3, out_res, 2 * out_res] template = tf.concat([test_conditions['uv_static'], test_conditions['uv_static']], axis=2) @@ -531,12 +531,12 @@ def plot_meshes(writer, garment_unposed, garment, mesh_gt, template, body, mask, collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment))] collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment[i], dtype=np.int32)*230), axis=0) for i in range(len(garment))] - for i in range(len(garment)): - writer.add_mesh('garment_pc_pred_' + str(i+nr_offset), vertices=garment[i][np.newaxis,:,:], global_step=step) - writer.add_mesh('garment_pred_' + str(i+nr_offset), vertices=garment[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step) - writer.add_mesh('template_' + str(i+nr_offset), vertices=template[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step) - writer.add_mesh('body_' + str(i+nr_offset), vertices=body[i][np.newaxis,:,:], faces=F[np.newaxis,:,:], global_step=step) - writer.add_mesh('collision_' + str(i+nr_offset), vertices=collision[i][np.newaxis,:,:], faces=collision_faces[i][np.newaxis,:,:], global_step=step, colors=collision_color[i][np.newaxis,:,:]) + # for i in range(len(garment)): + # writer.add_mesh('garment_pc_pred_' + str(i+nr_offset), vertices=garment[i][np.newaxis,:,:], global_step=step) + # writer.add_mesh('garment_pred_' + str(i+nr_offset), vertices=garment[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step) + # writer.add_mesh('template_' + str(i+nr_offset), vertices=template[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step) + # writer.add_mesh('body_' + str(i+nr_offset), vertices=body[i][np.newaxis,:,:], faces=F[np.newaxis,:,:], global_step=step) + # writer.add_mesh('collision_' + str(i+nr_offset), vertices=collision[i][np.newaxis,:,:], faces=collision_faces[i][np.newaxis,:,:], global_step=step, colors=collision_color[i][np.newaxis,:,:]) for i in range(len(garment)): plotly_fig = plot_3d(mesh_gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) @@ -576,8 +576,8 @@ def main(): train_resolutions, train_g_featuremaps = filter_resolutions_featuremaps(resolutions, g_featuremaps, args['train_res']) _, train_d_featuremaps = filter_resolutions_featuremaps(resolutions, d_featuremaps, args['train_res']) g_params = { - 'z_dim': 256, - 'w_dim': 256, + 'z_dim': 128, + 'w_dim': 128, 'labels_dim': -1, 'n_mapping': 8, 'resolutions': train_resolutions, @@ -644,7 +644,7 @@ def main(): 'd_params': d_params, # training params - 'g_opt': {'learning_rate': 0.002, 'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08, 'reg_interval': 8}, + 'g_opt': {'learning_rate': 0.001, 'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08, 'reg_interval': 1e10}, 'd_opt': {'learning_rate': 0.002, 'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08, 'reg_interval': 16}, 'batch_size': global_batch_size, 'n_total_image': 25000000, @@ -652,7 +652,7 @@ def main(): 'train_res': args['train_res'], } - trainer = Trainer(training_parameters, name=f'stylegan2-test-35-{args["train_res"]}x{args["train_res"]}') + trainer = Trainer(training_parameters, name=f'stylegan2-test-37_2-{args["train_res"]}x{args["train_res"]}') trainer.train(dist_dataset, static_dataset, signature_dynamic, strategy) return diff --git a/vae.py b/vae.py new file mode 100644 index 0000000..35cee89 --- /dev/null +++ b/vae.py @@ -0,0 +1,414 @@ +import os +import time +import argparse +import wandb +import random +import numpy as np +import scipy as sp +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, bend_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, sparse_to_tensor +from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor +from dataset import DatasetFactory, StaticDataset +from custom_utils.custom_utils import batch_to_dict +from custom_utils.smpl import SMPL + +name='vae_11' +os.environ["CUDA_VISIBLE_DEVICES"] = "1" +run = wandb.init(project='vae', name=name, + notes='vae, ms_ssim with range 3. with l1 loss, larger latent_dim=1024. 4 dowscaling steps, 4 upscaling, the image resize for final resolution. upscale from 256*128') +artifact = wandb.Artifact('code', type='code') +artifact.add_file('vae.py') +artifact.add_file('dataset.py') +artifact.add_file('losses.py') +artifact.add_file('convnext/models/convnext_tf.py') +run.log_artifact(artifact) + +uv2mesh = UV2Mesh(res=256) + +def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color): + mesh_pred_unposed[:,1] += 1 + collision[:,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_unposed[:,0], + y=mesh_pred_unposed[:,1], + z=mesh_pred_unposed[:,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=collision[:,0], + y=collision[:,1], + z=collision[:,2], + # i, j and k give the vertices of triangles + i = collision_faces[:,0], + j = collision_faces[:,1], + k = collision_faces[:,2], + vertexcolor=collision_color + ) + ]) + + 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 Encoder(tf.keras.Model): + + def __init__(self, latent_dim): + super(Encoder, self).__init__() + + self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, 128], include_top=False) + self.flatten = tf.keras.layers.Flatten() + self.mean = tf.keras.layers.Dense(latent_dim) + self.log_var = tf.keras.layers.Dense(latent_dim) + + def call(self, inputs, training=None): + x = inputs + + x = self.encoder(x) + x = self.flatten(x) + mean = self.mean(x) + log_var = self.log_var(x) + + return mean, log_var + + +class Decoder(tf.keras.Model): + + def __init__(self): + super(Decoder, self).__init__() + + self.dense = tf.keras.layers.Dense(8 * 4 * 512) + self.reshape = tf.keras.layers.Reshape((8, 4, 512)) + self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([128, 128, 64, 16]))) + + + def call(self, inputs, training=None): + x = inputs + + x = self.dense(x) + x = self.reshape(x) + x = self.decoder(x) + + return x + + +class VAE(tf.keras.Model): + + def __init__(self, latent_dim=1024): + super(VAE, self).__init__() + + + self.encoder = Encoder(latent_dim) + self.decoder = Decoder() + + + @tf.function + def sample(self, eps=None): + if eps is None: + eps = tf.random.normal(shape=(4, self.latent_dim)) + return self.decode(eps, apply_sigmoid=True) + + + def reparameterize(self, mean, logvar): + eps = tf.random.normal(shape=mean.shape) + return eps * tf.exp(logvar * .5) + mean + + + def decode(self, z, apply_sigmoid=False): + logits = self.decoder(z) + if apply_sigmoid: + probs = tf.sigmoid(logits) + return probs + return logits + + + def call(self, inputs, training=None): + x, uv_mask = inputs + + x = tf.transpose(x, [0,2,3,1]) + + mean, logvar = self.encoder(x) + z = self.reparameterize(mean, logvar) + x = self.decoder(z) + + + # x = tf.transpose(x, [0,3,1,2]) + # x = x * uv_mask[:,tf.newaxis,:,:] + x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5) + x = x * uv_mask[:,:,:,tf.newaxis] + + return x, mean, logvar, z + + +def log_normal_pdf(sample, mean, logvar, raxis=1): + log2pi = tf.math.log(2. * np.pi) + res = tf.reduce_sum(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi), axis=raxis) + return res + + +@tf.function +def train_step(batch): + + with tf.GradientTape() as tape: + + pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_mask']]) + + mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2])) + mesh_orig = batch['mesh_unposed'] + + + e_loss = 1e-3 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss = 1e-3 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask'])) + l1_loss = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred)), axis=-1) * batch['uv_mask'], axis=[1, 2]) + # ms_ssim = 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) + ms_ssim = tf.image.ssim_multiscale(pred + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3) + # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale((tf.transpose(pred, [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], 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_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(tf.transpose(batch['uv_unposed'], [0,2,3,1]), tf.zeros_like(tf.transpose(batch['uv_unposed'], [0,2,3,1]))))), 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']) + tv_loss = tf.image.total_variation(pred) + + reconstr_loss = l1_loss + logpx_z = -reconstr_loss + logpz = log_normal_pdf(z, 0., 0.) + logqz_x = log_normal_pdf(z, mean, logvar) + loss = -tf.reduce_mean(logpx_z + logpz - logqz_x) + ms_ssim = tf.reduce_mean(ms_ssim) + l1_loss = tf.reduce_mean(l1_loss) + fft_loss = tf.reduce_mean(fft_loss) + reconstr_loss = tf.reduce_mean(reconstr_loss) + tv_loss = tf.reduce_mean(tv_loss) + + + 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']) + 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, 'train/reconstr_loss': reconstr_loss, 'train/tv_loss': tv_loss } + + + +@tf.function +def valid_step(batch): + pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_mask']]) + + mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2])) + 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_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred)), axis=-1) * batch['uv_mask'], axis=[1, 2]) + b_loss = 0 #1e-3 * bend_loss(mesh_pred, smpl_faces, laplacian_loss, batch['mesh_mask']) + # ms_ssim = 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) + ms_ssim = tf.image.ssim_multiscale(pred + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3) + fft_loss = 0 #tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(tf.transpose(batch['uv_unposed'], [0,2,3,1]), tf.zeros_like(tf.transpose(batch['uv_unposed'], [0,2,3,1]))))), axis=[1,2,3]) + # 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 = 0 #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']) + + reconstr_loss = l1_loss + logpx_z = -reconstr_loss + logpz = log_normal_pdf(z, 0., 0.) + logqz_x = log_normal_pdf(z, mean, logvar) + loss = -tf.reduce_mean(logpx_z + logpz - logqz_x) + ms_ssim = tf.reduce_mean(ms_ssim) + l1_loss = tf.reduce_mean(l1_loss) + fft_loss = tf.reduce_mean(fft_loss) + b_loss = tf.reduce_mean(b_loss) + reconstr_loss = tf.reduce_mean(reconstr_loss) + + 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, 'train/bend_loss': b_loss, 'train/reconstr_loss': reconstr_loss + } + + +def plot_everything(batch: dict, type: str): + + pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_mask']]) + + mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2])) + mesh_orig = batch['mesh_unposed'] + + orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1]) + pred_img = pred + 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 } } + + + + + +def repeat_faces(faces, times): + idx_offset = np.linspace(0, 14475 * (times-1), times).astype(np.int32) + idx_offset = np.repeat(idx_offset, faces.shape[0], axis=0) + merged = np.tile(faces, [times,1]) + merged += idx_offset[:, np.newaxis] + return merged + + +def repeat_laplacian(L, times): + idx = np.repeat(np.mat([L.row, L.col]), repeats=times, axis=1) + idx_offset = np.linspace(0, L.shape[0] * (times-1), times).astype(np.int32) + idx_offset = np.repeat(idx_offset, L.row.shape[0], axis=0) + idx += idx_offset[np.newaxis,:] + data = np.repeat(L.data, repeats=times, axis=0) + shape = (L.shape[0] * times, L.shape[1] * times) + return sp.sparse.coo_matrix((data, (np.squeeze(np.asarray(idx[0,:])), np.squeeze(np.asarray(idx[1,:])))), shape) + + +if __name__ == '__main__': + allow_memory_growth() + parser = argparse.ArgumentParser(description='') + parser.add_argument('--name', type=str) + args = vars(parser.parse_args()) + + batch_size = 8 + + smpl_faces = np.load('custom_utils/smpl_faces.npy') + faces_loss_single = smpl_faces + faces_loss = repeat_faces(smpl_faces, batch_size) + laplacian_loss = sp.sparse.load_npz("custom_utils/laplacian/laplacian0.npz").todense().astype(np.float32) + # edges = np.stack(np.nonzero(laplacian_loss.todense())).transpose() + # laplacian_loss = sparse_to_tensor(laplacian_loss) + # laplacian_loss = sparse_to_tensor(repeat_laplacian(laplacian_loss, batch_size)) + + smpl = SMPL() + + 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=batch_size, + 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=batch_size, + signature=signature_dynamic) + static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', + signature=signature_static) + + + model = VAE() + lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False) + optimizer = tf.optimizers.Adam(learning_rate=lr_schedule) + folder = 'models/ae_logs/' + valid_iterator = iter(dataset_valid) + + step = 0 + for batch in dataset_train: + batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic)) + + # _, masked_faces = maskFacesBatch(batch['mesh_unposed'].numpy(), smpl_faces, batch['mesh_mask'].numpy().astype(bool)) + # # batch['mesh_masked_verts'] = masked_verts + # batch['mesh_masked_faces'] = [masked_faces[i].astype(np.int32) for i in range(batch_size)] + # batch['mesh_mask_bool'] = batch['mesh_mask'].numpy().astype(bool) + # batch['laplacian'] = [sparse_to_tensor(sp.sparse.coo_matrix(laplacian_loss[batch['mesh_mask_bool'][i], :][:, batch['mesh_mask_bool'][i]])) for i in range(batch_size)] + # batch['laplacian'] = [tf.sparse.to_dense(sparse_to_tensor(sp.sparse.coo_matrix(laplacian_loss[batch['mesh_mask_bool'][i], :][:, batch['mesh_mask_bool'][i]]))) for i in range(batch_size)] + # batch['laplacian'] = [sp.sparse.coo_matrix(laplacian_loss[batch['mesh_mask_bool'][i], :][:, batch['mesh_mask_bool'][i]]) for i in range(batch_size)] + + 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 % 500 == 0: + train_figs = plot_everything(batch, 'train') + valid_figs = plot_everything(data_valid, 'valid') + wandb.log({ **train_loss, **valid_loss, **{ 'current_step': step}, **train_figs, **valid_figs }, step=step) + else: + wandb.log({ **train_loss, **valid_loss, **{ 'current_step': step} }, step=step) + + + print(step) + + step += 1 \ No newline at end of file diff --git a/verify_scales.py b/verify_scales.py new file mode 100644 index 0000000..4ae144b --- /dev/null +++ b/verify_scales.py @@ -0,0 +1,81 @@ +import os +import sys +import time +import cv2 +import random +import numpy as np +import tensorflow as tf + +from dataset import DatasetFactory, StaticDataset +from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces +import plotly.graph_objects as go +import plotly.express as ex +import cmapy as cm + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = "6" + + uv2mesh = UV2Mesh() + + F = np.load("custom_utils/smpl_faces.npy") + + 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,), + # 'uv_curvature': (512, 256), + # 'mesh_curvature': (14475) + } + 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 = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train', + batch_size=1, + signature=signature_dynamic) + static_dataset = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train', + signature=signature_static) + + + for i, batch in enumerate(dataset): + print(i) + batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic)) + + uv_unposed = np.squeeze(batch['uv_unposed'].numpy()) + mesh_unposed = np.squeeze(batch['mesh_unposed'].numpy()) + mask = np.squeeze(batch['mesh_mask'].numpy()).astype(bool) + _, faces = _maskFaces(np.ones((14475, 3)), F, mask) + + mesh_unposed[:,1] += (1 * i) + fig = go.Figure(data=[ + go.Mesh3d( + # 8 vertices of a cube + x=mesh_unposed[mask,0], + y=mesh_unposed[mask,2], + z=mesh_unposed[mask,1], + # 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=10, z=1)) + fig.update_layout( + scene = dict( + xaxis = dict(nticks=4, range=[-1,1],), + yaxis = dict(nticks=4, range=[-1,19],), + zaxis = dict(nticks=4, range=[-1,1],),) + ) + + fig.show() + + print() \ No newline at end of file diff --git a/verify_uv_mapping.py b/verify_uv_mapping.py new file mode 100644 index 0000000..2d52cf2 --- /dev/null +++ b/verify_uv_mapping.py @@ -0,0 +1,145 @@ +import os +import sys +import time +from unicodedata import name +import cv2 +import random +import numpy as np +import tensorflow as tf + +from dataset import DatasetFactory, StaticDataset +from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces +import plotly.graph_objects as go +import plotly.express as ex +import cmapy as cm + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = "6" + + uv2mesh = UV2Mesh() + + F = np.load("custom_utils/smpl_faces.npy") + + 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,), + # 'uv_curvature': (512, 256), + # 'mesh_curvature': (14475) + } + 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 = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train', + batch_size=1, + signature=signature_dynamic) + static_dataset = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train', + signature=signature_static) + + + uv_res = 256 + + def m2uv_array(m2uv): + result = np.zeros((14475, 2), dtype=np.int32) + for k, v in m2uv.items(): + if len(v) == 2: + result[k] = list(v) + elif len(v) == 1: + x = v.pop() + result[k] = [x, x] + else: + assert False + + return result + + def unique_uv_map_mask(uv_map_discreet): + mask = np.zeros((uv_map_discreet.shape[0]), dtype=bool) + temp = np.zeros((uv_res * 2, uv_res)) + for p in uv_map_discreet: + temp[p[0], p[1]] += 1 + # temp = temp == 1 + for i, p in enumerate(uv_map_discreet): + if temp[p[0], p[1]] == 1: + mask[i] = True + elif temp[p[0], p[1]] == 2: + mask[i] = True + temp[p[0], p[1]] = 0 + return mask + + def uv_to_mesh(uv, m2uv): + res = uv.shape[0] + mesh = np.zeros((14475, 3)) + uv_pixels = uv_map_discreet[m2uv] + # counter = np.ones((14475)) + # counter[m2uv[:, 0] == m2uv[:, 1]] = 2 + mesh = np.mean(uv[uv_pixels[:, :, 0], uv_pixels[:, :, 1], :], axis=1) # / counter[:, np.newaxis] + return mesh + + + uv_map = np.load('custom_utils/stretched_uv_map_3.npy') + m2uv = np.load('custom_utils/m2uv.npy', allow_pickle=True).item() + m2uv = m2uv_array(m2uv) + uv_mask = np.load('custom_utils/mask_256x512.npy') + curvature_label_type = '6_25' + + uv_res = 256 + uv_mask = (cv2.resize(uv_mask * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis] + uv_mask = cv2.dilate(uv_mask.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)[:, :, np.newaxis] + + uv_map_discreet = np.floor(uv_map * uv_res).astype(np.int32) + uv_map_mask = unique_uv_map_mask(uv_map_discreet) + uv_counter = np.zeros((uv_map.shape[0])) + uv_counter[m2uv[:, 0]] = 1 + uv_counter[m2uv[:, 1]] += 1 + uv_counter = uv_counter[uv_map_mask] + + + + for i, batch in enumerate(dataset): + print(i) + batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic)) + + uv_unposed = np.squeeze(batch['uv_unposed'].numpy()) + mesh_unposed = np.squeeze(batch['mesh_unposed'].numpy()) + mask = np.squeeze(batch['mesh_mask'].numpy()).astype(bool) + + + values = mesh_unposed + uv_values = np.zeros((uv_map.shape[0], 3)) + uv_values[m2uv[:, 0]] = values + uv_values[m2uv[:, 1]] += values + uv_counter = np.zeros((uv_map.shape[0])) + uv_counter[m2uv[:, 0]] = 1 + uv_counter[m2uv[:, 1]] += 1 + uv_values /= uv_counter[:, np.newaxis] + x = uv_values + # verts = np.concatenate((verts, np.zeros((15068, 1))), axis=-1) + im = ((x - x.min()) / (x.max() - x.min())) * 255 + + + x = np.transpose(uv_unposed, [1,2,0]) + x = uv2mesh(uv_unposed[tf.newaxis,:,:,:])[0].numpy()[mask, :] + _, faces = _maskFaces(np.zeros((14475, 3), dtype=np.float32), F, mask) + go.Figure(data=[ + go.Mesh3d( + # 8 vertices of a cube + x=x[:,0], + y=x[:,1], + z=x[:,2], + # i, j and k give the vertices of triangles + i = faces[:,0], + j = faces[:,1], + k = faces[:,2] + )]).show() + + + print() \ No newline at end of file

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zSbzmsfCX591z3OuSbzmsfCX591z3OuSbzmsfCX591z3OuSbzmsfCX591z3OuSbzms bfCX591z3OuSbzmsfCX591z3Ou{uTHO9uJ%D literal 0 HcmV?d00001 diff --git a/custom_utils/__init__.py b/custom_utils/a__init__.py similarity index 100% rename from custom_utils/__init__.py rename to custom_utils/a__init__.py diff --git a/custom_utils/custom_utils.py b/custom_utils/custom_utils.py index e591e8e..fb8feb5 100644 --- a/custom_utils/custom_utils.py +++ b/custom_utils/custom_utils.py @@ -66,6 +66,33 @@ class UV2Mesh(): return self.uv_to_mesh(uv) +class UV2MeshLabel(): + def __init__(self, res=256): + super(UV2MeshLabel, self).__init__() + self.res = res + uv_map = np.load('custom_utils/stretched_uv_map_3.npy') + self.uv_map_discreet = np.floor(uv_map * self.res).astype(np.int32) + m2uv = np.load('custom_utils/m2uv.npy', allow_pickle=True).item() + m2uv = m2uv_array(m2uv) + self.uv_pixels = self.uv_map_discreet[m2uv] + + # input = channels_first + # @tf.function + def uv_to_mesh(self, uv): + batch_size = uv.shape[0] + + x1_ind = tf.tile(self.uv_pixels[:, 0, :][tf.newaxis, :, :], [batch_size, 1, 1]) + x2_ind = tf.tile(self.uv_pixels[:, 1, :][tf.newaxis, :, :], [batch_size, 1, 1]) + + x1 = tf.gather_nd(uv, x1_ind, batch_dims=1) + x2 = tf.gather_nd(uv, x2_ind, batch_dims=1) + + mesh = (x1 + x2) / 2 + return mesh + + def __call__(self, uv): + return self.uv_to_mesh(uv) + def _maskFaces(V, F, mask): V_mask = mask @@ -79,6 +106,21 @@ def _maskFaces(V, F, mask): return V_new, F_new + +# def _maskFaces_tf(V, F, mask): +# V_mask = mask +# F_mask = tf.gather(V_mask, F.reshape(-1), axis=0) +# F_mask = F_mask.reshape((-1, 3)).sum(axis=1) == 3 +# F_mask = tf.reduce_sum(tf.reshape(F_mask, (-1, 3)), axis=1) == 3 + +# V_ind = tf.zeros(V_mask.shape) +# V_ind[V_mask] = tf.range(V_mask.sum()) +# F_new = tf.gather(V_ind, F[F_mask, :].reshape(-1), axis=0).reshape((-1, 3)) +# V_new = V[V_mask, :] + +# return V_new, F_new + + def maskFacesBatch(V, F, mask): Vs = [] Fs = [] @@ -148,3 +190,10 @@ conditions = ['pose_shape', 'uv_static', 'uv_mask', 'uv_shaped_body'] def nest_conditions(batch): batch['conditions'] = {d: batch.pop(d) for d in conditions} return batch + + +def sparse_to_tensor(L): + idx = np.mat([L.row, L.col]).transpose().astype(np.float32) + data = L.data.astype(dtype=np.float32) + shape = L.shape + return tf.SparseTensor(idx, data, shape) \ No newline at end of file diff --git a/custom_utils/graph_utils.py b/custom_utils/graph_utils.py index 5e09d28..1113727 100644 --- a/custom_utils/graph_utils.py +++ b/custom_utils/graph_utils.py @@ -1,10 +1,14 @@ +import os +import sys import scipy as sp import numpy as np import scipy.io as sio import tensorflow as tf from tensorflow.keras.activations import tanh, relu from tensorflow.keras.layers import Dense, BatchNormalization, Add, Flatten -from custom_utils.custom_utils import UV2Mesh + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..') +# from custom_utils.custom_utils import UV2Mesh from tensorflow_graphics.geometry.convolution.graph_pooling import pool, unpool from tensorflow_graphics.geometry.convolution.graph_convolution import edge_convolution_template @@ -31,6 +35,14 @@ def repeat_laplacian(L, times): return sp.sparse.coo_matrix((data, (np.squeeze(np.asarray(idx[0,:])), np.squeeze(np.asarray(idx[1,:])))), shape) +def convert_pooling(pooling, batchSize): + pool_map = np.zeros((np.max(pooling)+1, pooling.shape[0]), dtype=np.float32) + for i in range(pooling.shape[0]): + pool_map[pooling[i], i] = 1.0 + pool_map = np.tile(pool_map[np.newaxis, :, :], [batchSize, 1, 1]) + pool_map = tf.sparse.from_dense(pool_map) + return pool_map + class GraphConvolution(tf.keras.layers.Layer): def __init__(self, shape, activation=None, name='gcn', L=None, batchSize=None, normalization=None): @@ -174,8 +186,76 @@ class GNNDiscriminator(tf.keras.Model): return x +class Reconstructor_V4_Decoder(tf.keras.Model): + def __init__(self, batchSize, isSkirt=False, n_feature=64): + super(Reconstructor_V4_Decoder, self).__init__() + laplacians = [sio.loadmat("custom_utils/laplacian/ssmpl" + ("_skirt" if isSkirt else "") + str(i) + ".mat")['L'].tocoo() for i in range(5)] + poolings = [sio.loadmat("custom_utils/pooling/pool" + str(i) + str(i+1) + ".mat")['idx'] for i in range(5)] + poolings = [convert_pooling(poolings[i][0,:], batchSize) for i in range(len(poolings))] + + self.custom_layers = [] + for i in range(4,1,-1): + self.custom_layers.append(GraphUnPool(pool_map=poolings[i])) + self.custom_layers.append(GraphConvolution((int(n_feature/2**(4-(i if i == 4 else i+1))), int(n_feature/2**(4-i)) ), activation=relu, L=laplacians[i], batchSize=batchSize, normalization=BatchNormalization())) + for _ in range(2**(i-2)): + conv1 = GraphConvolution((int(n_feature/2**(4-i)), int(n_feature/2**(4-i))), activation=relu, L=laplacians[i], batchSize=batchSize, normalization=BatchNormalization()) + conv2 = GraphConvolution((int(n_feature/2**(4-i)), int(n_feature/2**(4-i))), activation=relu, L=laplacians[i], batchSize=batchSize, normalization=BatchNormalization()) + self.custom_layers.append([conv1, conv2]) + + self.pool2 = GraphUnPool(pool_map=poolings[1]) + self.conv2 = GraphConvolution((int(n_feature/2**2), int(n_feature/2**2)), activation=relu, L=laplacians[1], batchSize=batchSize, normalization=BatchNormalization(), name='gcn_2') + + self.pool1 = GraphUnPool(pool_map=poolings[0]) + self.conv1 = GraphConvolution((int(n_feature/2**2), int(n_feature/2**3)), activation=relu, L=laplacians[0], batchSize=batchSize, normalization=BatchNormalization(), name='gcn_1') + + self.conv0 = GraphConvolution((int(n_feature/2**3), 3), L=laplacians[0], batchSize=batchSize, name='gcn_0') + + def call(self, inputs, training=None): + x = inputs + + for layer in self.custom_layers: + if isinstance(layer, list): + x1 = layer[0](x, training) + x2 = layer[1](x1, training) + x = Add()([x, x2]) + else: + x = layer(x, training=training) + + x = self.pool2(x) + x = self.conv2(x, training) + + x = self.pool1(x) + x = self.conv1(x, training) + + x = self.conv0(x, training) + + return x + + + +class Reconstructor_v2(tf.keras.Model): + def __init__(self, batchSize, isSkirt=False): + super(Reconstructor_v2, self).__init__() + + self.flatten = tf.keras.layers.Flatten() + + self.fc2 = tf.keras.layers.Dense(14475*3, kernel_regularizer=tf.keras.regularizers.l2) + + self.reshape2 = tf.keras.layers.Reshape((14475, 3)) + # self.drop = Dropout(0.2) + # self.fc3 = Dense(512) + self.fc1 = tf.keras.layers.Dense(3) + + def call(self, inputs, training=None): + x = self.flatten(inputs) + x = self.fc2(x) + # x = self.drop(x, training=training) + x = self.reshape2(x) + # x = self.fc3(x) + x = self.fc1(x) + return x class DatasetTest(tf.data.Dataset): @@ -195,13 +275,16 @@ class DatasetTest(tf.data.Dataset): if __name__ == '__main__': import os - os.environ["CUDA_VISIBLE_DEVICES"] = "1" + os.environ["CUDA_VISIBLE_DEVICES"] = "2" batch_size = 4 - input = tf.ones((batch_size, 14475, 3)) + # input = tf.ones((batch_size, 14475, 3)) - ge = GNNDiscriminator(batchSize=batch_size, n_feature=32) + # ge = GNNDiscriminator(batchSize=batch_size, n_feature=32) - x = ge(input) + # x = ge(input) + + gd = Reconstructor_V4_Decoder(batch_size) + x = gd(tf.ones((batch_size, 21, 64))) print() \ No newline at end of file diff --git a/custom_utils/laplacian/laplacian0.npz b/custom_utils/laplacian/laplacian0.npz new file mode 100644 index 0000000000000000000000000000000000000000..565c3cdba99d365ef44812a148e0ecf11f8c004b GIT binary patch literal 188972 zcmc${cUaT+_r^^&A&?OE4k0oWgdhrG$O?i31&bq&LISu7N)Sg-gb)IWf)MM%iUMlY z)WSg8aTu_8R5PX+|Vw%_0NJlErO=^x;KhjY&BzR&#*v2$Q> zGd(>$ec&%q&*RMmvjcQJJu6#;9!k$lZ++Tk&vnd9h@P?POThop1HTRaDSPzxwPkW^ z$T!blz7)JPnGrg{Fx+O3$*hTM!uA+f&qA1fZ-zK_Y{8YiCaG|<*oo!~;0r>7I(%N= zw7dPG{r<&%gfT`K^bK?w+HbvyE(5(4HT@R`Awp>EHK`eE;IjnfX`3 zfBE$q%E&(W!b8#7yn=VVZ*%VS_4?9jRl`1ndnX>s;)hRoWz2c|-S0mxbX!*0Q+i}L z#QmdD?re))c3(vttDW~F{0Gvn@MA_Z_d9Om6bU9eWrXfypT<9cuAk6y!14aZ^{y?p 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