text stringlengths 1 93.6k |
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return image, label
|
dataset = dataset.map(map_fn)
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return dataset
|
def factory_input_fn(params, is_training):
|
if params['data_source'] == 'tfr':
|
return tfr_input_fn(params, is_training)
|
elif params['data_source'] in ['mnist', 'cifar10', 'cifar100', 'lsun']:
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return tfds_input_fn(params, params['data_source'], is_training)
|
# --------------------------------------------------------------------------
|
# Parse tf records
|
# --------------------------------------------------------------------------
|
def parse_tfrecord(params, record):
|
if params["tfr_format"] == 'progan':
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return parse_tfrecord_progan(params, record)
|
elif params["tfr_format"] == 'inception':
|
return parse_tfrecord_inception(
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params, record,
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width=params['img_size'],
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height=params['img_size'],
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is_training=False, use_summary=params['use_summary'])
|
else:
|
raise NotImplementedError("Unrecognised --tfr_format")
|
def parse_tfrecord_progan(params, record):
|
'''
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Parse the records saved using the NVIDIA ProGAN dataset_tool.py
|
Data is stored as CHW uint8 with values ranging 0-255
|
Size is stored beside image byte strings
|
Data is stored in files with suffix -rN.tfrecords
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N = 0 is the largest size, 128x128 in my personal image build
|
'''
|
features = tf.parse_single_example(record, features={
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'shape': tf.FixedLenFeature([3], tf.int64),
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'data': tf.FixedLenFeature([], tf.string)})
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data = tf.decode_raw(features['data'], tf.uint8)
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# img = tf.reshape(data, features['shape']) # The way from ProGAN
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img = tf.reshape(data, [params['img_ch'], params['img_size'], params['img_size']])
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img = tf.transpose(img, [1,2,0]) # CHW => HWC
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img = tf.cast(img, tf.float32) / 127.5 - 1
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empty_label = tf.constant([params['batch_size'], params['num_labels']], dtype=img.dtype)
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return img, empty_label
|
def parse_tfrecord_inception(params, record, width, height, is_training=True, use_summary=False):
|
'''
|
Parse the records saved using the tensorflow official inception data build
|
ERROR: type should be string, got "\thttps://github.com/tensorflow/models\n" |
'''
|
image_buffer, label, bbox, label_text = parse_example_proto(record)
|
image = image_preprocessing(image_buffer, bbox, is_training, width, height, use_summary=use_summary)
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# [batch, height, width, channels] range(-1.0,1.0)
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label_one_hot = tf.one_hot(tf.squeeze(label, axis=-1), params['num_labels'], dtype=image.dtype)
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return image, label_one_hot
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# <FILESEP>
|
import numpy as np
|
import constants
|
from utils.geometry import perspective_projection
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import torch
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def get_2d_joints(joints_3d, pred_cam_t, focal_length,dev, batch_size ):
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rotation = torch.eye(3, device=dev).unsqueeze(0).expand(batch_size, -1, -1)
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camera_center = torch.zeros(batch_size, 2, device=dev)
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print('cam)t',pred_cam_t)
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print('rotaion',rotation)
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pred_keypoints_2d = perspective_projection(joints_3d,
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rotation=rotation,
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translation=pred_cam_t,
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focal_length=focal_length,
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camera_center=camera_center)
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return pred_keypoints_2d
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def get_original(proc_parm, verts, cam, joints):
|
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