steffen/masks_augments_compare-21
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New Model, Many Changes

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Si11ium 2020-11-22 16:24:00 +01:00
parent be097a111a
commit a079a196af
3 changed files with 39 additions and 43 deletions
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2
_paramters.py
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@ -52,7 +52,7 @@ main_arg_parser.add_argument("--model_features", type=int, default=64, help="")
main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="") main_arg_parser.add_argument("--model_filters", type=str, default="[32, 64, 128, 64]", help="")
# Transformer Specific # Transformer Specific
main_arg_parser.add_argument("--model_patch_size", type=int, default=9, help="") main_arg_parser.add_argument("--model_patch_size", type=int, default=3, help="")
main_arg_parser.add_argument("--model_attn_depth", type=int, default=3, help="") main_arg_parser.add_argument("--model_attn_depth", type=int, default=3, help="")
main_arg_parser.add_argument("--model_heads", type=int, default=8, help="") main_arg_parser.add_argument("--model_heads", type=int, default=8, help="")
main_arg_parser.add_argument("--model_embedding_size", type=int, default=64, help="") main_arg_parser.add_argument("--model_embedding_size", type=int, default=64, help="")

19
models/transformer_model_sequential.py
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@ -5,8 +5,6 @@ import warnings
import torch import torch
from torch import nn from torch import nn
from einops import repeat
from ml_lib.modules.blocks import TransformerModule from ml_lib.modules.blocks import TransformerModule
from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow) from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin, from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
@ -39,16 +37,13 @@ class SequentialVisualTransformer(BinaryMaskDatasetMixin,
self.embed_dim = self.params.embedding_size self.embed_dim = self.params.embedding_size
self.patch_size = self.params.patch_size self.patch_size = self.params.patch_size
self.height = height self.height = height
self.width = width
self.channels = channels
# Automatic Image Shaping self.new_width = ((self.width - self.patch_size)//1) + 1
image_size = (max(height, width) // self.patch_size) * self.patch_size
self.image_size = image_size + self.patch_size if image_size < max(height, width) else image_size
# This should be obsolete num_patches = self.new_width - (self.patch_size // 2)
assert self.image_size % self.patch_size == 0, 'image dimensions must be divisible by the patch size' patch_dim = channels * self.patch_size * self.height
num_patches = (self.image_size // self.patch_size) ** 2
patch_dim = channels * self.patch_size * self.image_size
assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \ assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
f'attention. Try decreasing your patch size' f'attention. Try decreasing your patch size'
@ -61,9 +56,9 @@ class SequentialVisualTransformer(BinaryMaskDatasetMixin,
func(message) func(message)
# Utility Modules # Utility Modules
self.autopad = AutoPadToShape((self.image_size, self.image_size)) self.autopad = AutoPadToShape((self.height, self.new_width))
self.dropout = nn.Dropout(self.params.dropout) self.dropout = nn.Dropout(self.params.dropout)
self.slider = SlidingWindow((self.image_size, self.patch_size), keepdim=False) self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.height, self.patch_size), keepdim=False)
# Modules with Parameters # Modules with Parameters
self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim, self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,

61
multi_run.py
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@ -20,42 +20,43 @@ if __name__ == '__main__':
config = Config().read_namespace(args) config = Config().read_namespace(args)
arg_dict = dict() arg_dict = dict()
for seed in range(0, 10): for seed in range(0, 3):
arg_dict.update(main_seed=seed) arg_dict.update(main_seed=seed)
for model in ['VisualTransformer']: for patch_size in [3, 5 , 9]:
arg_dict.update(model_type=model) for model in ['SequentialVisualTransformer']:
raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0, arg_dict.update(model_type=model, model_patch_size=patch_size)
data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0, raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0,
data_stretch=False, train_epochs=401) data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
data_stretch=False, train_epochs=401)
all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7, all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4, data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4,
data_stretch=True, train_epochs=101) data_stretch=True, train_epochs=101)
speed_conf = raw_conf.copy() speed_conf = raw_conf.copy()
speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7, speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
data_stretch=True, train_epochs=101) data_stretch=True, train_epochs=101)
mask_conf = raw_conf.copy() mask_conf = raw_conf.copy()
mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101) mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101)
noise_conf = raw_conf.copy() noise_conf = raw_conf.copy()
noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101) noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101)
shift_conf = raw_conf.copy() shift_conf = raw_conf.copy()
shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101) shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101)
loudness_conf = raw_conf.copy() loudness_conf = raw_conf.copy()
loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101) loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101)
for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]: for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]:
arg_dict.update(dicts) arg_dict.update(dicts)
config = config.update(arg_dict) config = config.update(arg_dict)
version_path = config.exp_path / config.version version_path = config.exp_path / config.version
if version_path.exists(): if version_path.exists():
if not (version_path / 'weights.ckpt').exists(): if not (version_path / 'weights.ckpt').exists():
shutil.rmtree(version_path) shutil.rmtree(version_path)
else: else:
continue continue
run_lightning_loop(config) run_lightning_loop(config)