Quality control of sc/snRNA-seq
Mariano Ruz Jurado
Goethe UniversityDOtools.RmdInstallation
DOtools is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:
install.packages("BiocManager") # WORK iN PROGRESS
BiocManager::install("DOtools")Alternatively, you can instead install the latest development version from GitHub with:
install.packages("devtools")
devtools::install_github("MarianoRuzJurado/DOtools")Usage
DOtools contains different functions for processing and visualizing gene expression in scRNA/snRNA experiments:
In this vignette we showcase how to use the functions with public available data.
Libraries
DOtools can be imported as:
library(DOtools)
#Additional packages
library(Seurat)
library(plyr)
library(dplyr)
library(tibble)
library(enrichR)
library(kableExtra)
#Python installation set up
DO.PyEnv()
#> 2025-06-26 17:46:45 - Using existing conda environment at: /home/mariano/.venv/DOtools
#> 2025-06-26 17:46:45 - Python packages ready for DOtools!
reticulate::use_python("~/.venv/DOtools/bin/python")Quality control
DOtools
The DO.Import() function provides a streamlined pipeline
for performing quality control on single-cell or single-nucleus RNA
sequencing (sc/snRNA-seq) data. It takes as input a list of .h5 files
generated by e.g. CellRanger or STARsolo, along with sample names and
metadata.
During preprocessing, low-quality genes and cells are filtered out based on specified thresholds. Genes expressed in fewer than five cells are removed. Cells are filtered according to mitochondrial gene content, number of detected genes, total UMI counts, and potential doublets. The function supports doublet detection using scDblFinder. Thresholds for mitochondrial content (e.g., 5% for scRNA-seq and 3% for snRNA-seq), gene counts, and UMI counts can be defined to tailor the filtering.
After filtering, samples are merged into one Seurat object, followed by log-normalisation, scaling, and the identification of highly variable genes. To help assess the effect of quality control, violin plots showing distributions of key metrics before and after filtering are automatically generated and saved alongside the input files. A summary of removed genes and cells is also recorded.
To show how the quality control works, we are going to use a public dataset from 10X from human blood of healthy and donors with a malignant tumor:
base <- DOtools:::.example_10x()
#> 📥 Downloading data to /tmp/Rtmp9zSWgB/dotools_datasets_1693241a7883e1
#> ⬇️ Downloading healthy filtered to /tmp/Rtmp9zSWgB/dotools_datasets_1693241a7883e1/healthy/outs/filtered_feature_bc_matrix.h5
#> ⬇️ Downloading healthy raw to /tmp/Rtmp9zSWgB/dotools_datasets_1693241a7883e1/healthy/outs/raw_feature_bc_matrix.h5
#> ⬇️ Downloading disease filtered to /tmp/Rtmp9zSWgB/dotools_datasets_1693241a7883e1/disease/outs/filtered_feature_bc_matrix.h5
#> ⬇️ Downloading disease raw to /tmp/Rtmp9zSWgB/dotools_datasets_1693241a7883e1/disease/outs/raw_feature_bc_matrix.h5
paths = c(file.path(base, "healthy/outs/filtered_feature_bc_matrix.h5"),
file.path(base, "disease/outs/filtered_feature_bc_matrix.h5"))
Seu_obj <- DO.Import(pathways = paths,
ids = c("healthy-1", "disease-1"),
TenX = T,
DeleteDoublets = T,
cut_mt = .05,
min_counts = 500,
min_genes = 300,
high_quantile = .95)
#> 2025-06-26 17:46:50 - Sample: healthy-1
#> 2025-06-26 17:46:50 - Read matrix
#> Genome matrix has multiple modalities, returning a list of matrices for this genome
#> 2025-06-26 17:46:55 - Create Seurat
#> 2025-06-26 17:46:56 - Setting condition in object to: healthy
#> 2025-06-26 17:46:56 - Get Mitochondrial+Ribosomal content
#> 2025-06-26 17:46:57 - Create QC images
#> 2025-06-26 17:46:58 - Start Filtering
#> 2025-06-26 17:47:00 - Running Normalisation
#> 2025-06-26 17:47:01 - Running Variable Gene Detection
#> 2025-06-26 17:47:01 - Running scDblFinder
#> Creating ~1712 artificial doublets...
#> Dimensional reduction
#> Evaluating kNN...
#> Training model...
#> iter=0, 255 cells excluded from training.
#> iter=1, 232 cells excluded from training.
#> iter=2, 210 cells excluded from training.
#> Threshold found:0.337
#> 64 (3%) doublets called
#> 2025-06-26 17:47:11 - Sample: disease-1
#> 2025-06-26 17:47:11 - Read matrix
#> Genome matrix has multiple modalities, returning a list of matrices for this genome
#> 2025-06-26 17:47:14 - Create Seurat
#> 2025-06-26 17:47:14 - Setting condition in object to: disease
#> 2025-06-26 17:47:14 - Get Mitochondrial+Ribosomal content
#> 2025-06-26 17:47:15 - Create QC images
#> 2025-06-26 17:47:16 - Start Filtering
#> 2025-06-26 17:47:17 - Running Normalisation
#> 2025-06-26 17:47:18 - Running Variable Gene Detection
#> 2025-06-26 17:47:18 - Running scDblFinder
#> Creating ~1500 artificial doublets...
#> Dimensional reduction
#> Evaluating kNN...
#> Training model...
#> iter=0, 51 cells excluded from training.
#> iter=1, 25 cells excluded from training.
#> iter=2, 25 cells excluded from training.
#> Threshold found:0.579
#> 21 (2.8%) doublets called
#> 2025-06-26 17:47:24 - Merging objects
#> 2025-06-26 17:47:24 - Running ScaleData
#> Centering and scaling data matrix
#> 2025-06-26 17:47:25 - Run PCA
#> Splitting 'counts', 'data' layers. Not splitting 'scale.data'. If you would like to split other layers, set in `layers` argument.
prefilterplots <- list.files(path = base, pattern = "prefiltered.*\\.svg$", full.names = TRUE, recursive = TRUE)
postfilterplots <- list.files(path = base, pattern = "postfiltered.*\\.svg$", full.names = TRUE, recursive = TRUE)We can now check the quality before introducing filterings:
pQC1 <- magick::image_read_svg(prefilterplots[1])
plot(pQC1)
And after:
pQC2 <- magick::image_read_svg(postfilterplots[1])
plot(pQC2)
In Addition, we can observe how similar the samples are through
running a correlation analysis.
DO.Correlation(Seu_obj)
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
We observed that most cells were removed due to increased mitochondrial content. Depending on the experimental design, the mitochondrial content threshold can be adjusted to retain a higher number of cells, if minimizing cell loss is of relevance.
Data integration
After quality control the prefered integration method can be chosen
within Seurat’s IntegrateLayers function. Additionally, we
implemented a new wrapper function for the scVI Integration from the scvi-tools package.
After the integration completes, we run the Leiden algorithm to find
clusters and generate UMAP embeddings.
#Integration through Seurat
Seu_obj <- IntegrateLayers(object = Seu_obj,
method = CCAIntegration,
orig.reduction = "pca",
new.reduction = "integrated.cca",
verbose = T)
#> Finding all pairwise anchors
#> Running CCA
#> Merging objects
#> Finding neighborhoods
#> Finding anchors
#> Found 2808 anchors
#> Merging dataset 2 into 1
#> Extracting anchors for merged samples
#> Finding integration vectors
#> Finding integration vector weights
#> Integrating data
#After Integration we join the layers
Seu_obj <- JoinLayers(Seu_obj)
#Integration with scVI
Seu_obj <- DO.scVI(Seu_object = Seu_obj,
batch_key ="orig.ident",
layer_counts = "counts",
layer_logcounts = "logcounts")
#> ℹ Using the 'counts' assay as the X matrix
#> Run scVI
#> Anndata setup with scvi-tools version 1.3.0.
#>
#> Setup via `SCVI.setup_anndata` with arguments:
#> {
#> │ 'layer': 'counts',
#> │ 'batch_key': 'orig.ident',
#> │ 'labels_key': None,
#> │ 'size_factor_key': None,
#> │ 'categorical_covariate_keys': None,
#> │ 'continuous_covariate_keys': None
#> }
#>
#> Summary Statistics
#> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓
#> ┃ Summary Stat Key ┃ Value ┃
#> ┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩
#> │ n_batch │ 2 │
#> │ n_cells │ 2807 │
#> │ n_extra_categorical_covs │ 0 │
#> │ n_extra_continuous_covs │ 0 │
#> │ n_labels │ 1 │
#> │ n_vars │ 2000 │
#> └──────────────────────────┴───────┘
#> Data Registry
#> ┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
#> ┃ Registry Key ┃ scvi-tools Location ┃
#> ┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
#> │ X │ adata.layers['counts'] │
#> │ batch │ adata.obs['_scvi_batch'] │
#> │ labels │ adata.obs['_scvi_labels'] │
#> └──────────────┴───────────────────────────┘
#> batch State Registry
#> ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
#> ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃
#> ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
#> │ adata.obs['orig.ident'] │ disease-1 │ 0 │
#> │ │ healthy-1 │ 1 │
#> └─────────────────────────┴────────────┴─────────────────────┘
#> labels State Registry
#> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
#> ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃
#> ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
#> │ adata.obs['_scvi_labels'] │ 0 │ 0 │
#> └───────────────────────────┴────────────┴─────────────────────┘
#> Training: 0%| | 0/400 [00:00<?, ?it/s]Epoch 1/400: 0%| | 0/400 [00:00<?, ?it/s]Epoch 1/400: 0%| | 1/400 [00:00<02:39, 2.51it/s]Epoch 1/400: 0%| | 1/400 [00:00<02:39, 2.51it/s, v_num=1, train_loss_step=843, train_loss_epoch=1.09e+3]Epoch 2/400: 0%| | 1/400 [00:00<02:39, 2.51it/s, v_num=1, train_loss_step=843, train_loss_epoch=1.09e+3]Epoch 2/400: 0%| | 2/400 [00:00<01:50, 3.60it/s, v_num=1, train_loss_step=843, train_loss_epoch=1.09e+3]Epoch 2/400: 0%| | 2/400 [00:00<01:50, 3.60it/s, v_num=1, train_loss_step=657, train_loss_epoch=765] Epoch 3/400: 0%| | 2/400 [00:00<01:50, 3.60it/s, v_num=1, train_loss_step=657, train_loss_epoch=765]Epoch 3/400: 1%| | 3/400 [00:00<01:33, 4.24it/s, v_num=1, train_loss_step=657, train_loss_epoch=765]Epoch 3/400: 1%| | 3/400 [00:00<01:33, 4.24it/s, v_num=1, train_loss_step=682, train_loss_epoch=665]Epoch 4/400: 1%| | 3/400 [00:00<01:33, 4.24it/s, v_num=1, train_loss_step=682, train_loss_epoch=665]Epoch 4/400: 1%| | 4/400 [00:00<01:27, 4.53it/s, v_num=1, train_loss_step=682, train_loss_epoch=665]Epoch 4/400: 1%| | 4/400 [00:00<01:27, 4.53it/s, v_num=1, train_loss_step=633, train_loss_epoch=630]Epoch 5/400: 1%| | 4/400 [00:00<01:27, 4.53it/s, v_num=1, train_loss_step=633, train_loss_epoch=630]Epoch 5/400: 1%|▏ | 5/400 [00:01<01:21, 4.84it/s, v_num=1, train_loss_step=633, train_loss_epoch=630]Epoch 5/400: 1%|▏ | 5/400 [00:01<01:21, 4.84it/s, v_num=1, train_loss_step=624, train_loss_epoch=615]Epoch 6/400: 1%|▏ | 5/400 [00:01<01:21, 4.84it/s, v_num=1, train_loss_step=624, train_loss_epoch=615]Epoch 6/400: 2%|▏ | 6/400 [00:01<01:16, 5.17it/s, v_num=1, train_loss_step=624, train_loss_epoch=615]Epoch 6/400: 2%|▏ | 6/400 [00:01<01:16, 5.17it/s, v_num=1, train_loss_step=611, train_loss_epoch=608]Epoch 7/400: 2%|▏ | 6/400 [00:01<01:16, 5.17it/s, v_num=1, train_loss_step=611, train_loss_epoch=608]Epoch 7/400: 2%|▏ | 7/400 [00:01<01:12, 5.45it/s, v_num=1, train_loss_step=611, train_loss_epoch=608]Epoch 7/400: 2%|▏ | 7/400 [00:01<01:12, 5.45it/s, v_num=1, train_loss_step=599, train_loss_epoch=602]Epoch 8/400: 2%|▏ | 7/400 [00:01<01:12, 5.45it/s, v_num=1, train_loss_step=599, train_loss_epoch=602]Epoch 8/400: 2%|▏ | 8/400 [00:01<01:08, 5.71it/s, v_num=1, train_loss_step=599, train_loss_epoch=602]Epoch 8/400: 2%|▏ | 8/400 [00:01<01:08, 5.71it/s, v_num=1, train_loss_step=634, train_loss_epoch=598]Epoch 9/400: 2%|▏ | 8/400 [00:01<01:08, 5.71it/s, v_num=1, train_loss_step=634, train_loss_epoch=598]Epoch 9/400: 2%|▏ | 9/400 [00:01<01:06, 5.88it/s, v_num=1, train_loss_step=634, train_loss_epoch=598]Epoch 9/400: 2%|▏ | 9/400 [00:01<01:06, 5.88it/s, v_num=1, train_loss_step=628, train_loss_epoch=596]Epoch 10/400: 2%|▏ | 9/400 [00:01<01:06, 5.88it/s, v_num=1, train_loss_step=628, train_loss_epoch=596]Epoch 10/400: 2%|▎ | 10/400 [00:01<01:04, 6.01it/s, v_num=1, train_loss_step=628, train_loss_epoch=596]Epoch 10/400: 2%|▎ | 10/400 [00:01<01:04, 6.01it/s, v_num=1, train_loss_step=619, train_loss_epoch=593]Epoch 11/400: 2%|▎ | 10/400 [00:01<01:04, 6.01it/s, v_num=1, train_loss_step=619, train_loss_epoch=593]Epoch 11/400: 3%|▎ | 11/400 [00:02<01:03, 6.12it/s, v_num=1, train_loss_step=619, train_loss_epoch=593]Epoch 11/400: 3%|▎ | 11/400 [00:02<01:03, 6.12it/s, v_num=1, train_loss_step=606, train_loss_epoch=590]Epoch 12/400: 3%|▎ | 11/400 [00:02<01:03, 6.12it/s, v_num=1, train_loss_step=606, train_loss_epoch=590]Epoch 12/400: 3%|▎ | 12/400 [00:02<01:03, 6.11it/s, v_num=1, train_loss_step=606, train_loss_epoch=590]Epoch 12/400: 3%|▎ | 12/400 [00:02<01:03, 6.11it/s, v_num=1, train_loss_step=586, train_loss_epoch=589]Epoch 13/400: 3%|▎ | 12/400 [00:02<01:03, 6.11it/s, v_num=1, train_loss_step=586, train_loss_epoch=589]Epoch 13/400: 3%|▎ | 13/400 [00:02<01:06, 5.84it/s, v_num=1, train_loss_step=586, train_loss_epoch=589]Epoch 13/400: 3%|▎ | 13/400 [00:02<01:06, 5.84it/s, v_num=1, train_loss_step=603, train_loss_epoch=587]Epoch 14/400: 3%|▎ | 13/400 [00:02<01:06, 5.84it/s, v_num=1, train_loss_step=603, train_loss_epoch=587]Epoch 14/400: 4%|▎ | 14/400 [00:02<01:08, 5.64it/s, v_num=1, train_loss_step=603, train_loss_epoch=587]Epoch 14/400: 4%|▎ | 14/400 [00:02<01:08, 5.64it/s, v_num=1, train_loss_step=575, train_loss_epoch=585]Epoch 15/400: 4%|▎ | 14/400 [00:02<01:08, 5.64it/s, v_num=1, train_loss_step=575, train_loss_epoch=585]Epoch 15/400: 4%|▍ | 15/400 [00:02<01:09, 5.58it/s, v_num=1, train_loss_step=575, train_loss_epoch=585]Epoch 15/400: 4%|▍ | 15/400 [00:02<01:09, 5.58it/s, v_num=1, train_loss_step=608, train_loss_epoch=583]Epoch 16/400: 4%|▍ | 15/400 [00:02<01:09, 5.58it/s, v_num=1, train_loss_step=608, train_loss_epoch=583]Epoch 16/400: 4%|▍ | 16/400 [00:03<01:09, 5.55it/s, v_num=1, train_loss_step=608, train_loss_epoch=583]Epoch 16/400: 4%|▍ | 16/400 [00:03<01:09, 5.55it/s, v_num=1, train_loss_step=601, train_loss_epoch=582]Epoch 17/400: 4%|▍ | 16/400 [00:03<01:09, 5.55it/s, v_num=1, train_loss_step=601, train_loss_epoch=582]Epoch 17/400: 4%|▍ | 17/400 [00:03<01:10, 5.42it/s, v_num=1, train_loss_step=601, train_loss_epoch=582]Epoch 17/400: 4%|▍ | 17/400 [00:03<01:10, 5.42it/s, v_num=1, train_loss_step=559, train_loss_epoch=581]Epoch 18/400: 4%|▍ | 17/400 [00:03<01:10, 5.42it/s, v_num=1, train_loss_step=559, train_loss_epoch=581]Epoch 18/400: 4%|▍ | 18/400 [00:03<01:10, 5.44it/s, v_num=1, train_loss_step=559, train_loss_epoch=581]Epoch 18/400: 4%|▍ | 18/400 [00:03<01:10, 5.44it/s, v_num=1, train_loss_step=562, train_loss_epoch=580]Epoch 19/400: 4%|▍ | 18/400 [00:03<01:10, 5.44it/s, v_num=1, train_loss_step=562, train_loss_epoch=580]Epoch 19/400: 5%|▍ | 19/400 [00:03<01:10, 5.41it/s, v_num=1, train_loss_step=562, train_loss_epoch=580]Epoch 19/400: 5%|▍ | 19/400 [00:03<01:10, 5.41it/s, v_num=1, train_loss_step=597, train_loss_epoch=578]Epoch 20/400: 5%|▍ | 19/400 [00:03<01:10, 5.41it/s, v_num=1, train_loss_step=597, train_loss_epoch=578]Epoch 20/400: 5%|▌ | 20/400 [00:03<01:07, 5.60it/s, v_num=1, train_loss_step=597, train_loss_epoch=578]Epoch 20/400: 5%|▌ | 20/400 [00:03<01:07, 5.60it/s, v_num=1, train_loss_step=546, train_loss_epoch=578]Epoch 21/400: 5%|▌ | 20/400 [00:03<01:07, 5.60it/s, v_num=1, train_loss_step=546, train_loss_epoch=578]Epoch 21/400: 5%|▌ | 21/400 [00:03<01:08, 5.50it/s, v_num=1, train_loss_step=546, train_loss_epoch=578]Epoch 21/400: 5%|▌ | 21/400 [00:03<01:08, 5.50it/s, v_num=1, train_loss_step=562, train_loss_epoch=577]Epoch 22/400: 5%|▌ | 21/400 [00:03<01:08, 5.50it/s, v_num=1, train_loss_step=562, train_loss_epoch=577]Epoch 22/400: 6%|▌ | 22/400 [00:04<01:07, 5.61it/s, v_num=1, train_loss_step=562, train_loss_epoch=577]Epoch 22/400: 6%|▌ | 22/400 [00:04<01:07, 5.61it/s, v_num=1, train_loss_step=573, train_loss_epoch=576]Epoch 23/400: 6%|▌ | 22/400 [00:04<01:07, 5.61it/s, v_num=1, train_loss_step=573, train_loss_epoch=576]Epoch 23/400: 6%|▌ | 23/400 [00:04<01:08, 5.51it/s, v_num=1, train_loss_step=573, train_loss_epoch=576]Epoch 23/400: 6%|▌ | 23/400 [00:04<01:08, 5.51it/s, v_num=1, train_loss_step=561, train_loss_epoch=575]Epoch 24/400: 6%|▌ | 23/400 [00:04<01:08, 5.51it/s, v_num=1, train_loss_step=561, train_loss_epoch=575]Epoch 24/400: 6%|▌ | 24/400 [00:04<01:08, 5.52it/s, v_num=1, train_loss_step=561, train_loss_epoch=575]Epoch 24/400: 6%|▌ | 24/400 [00:04<01:08, 5.52it/s, v_num=1, train_loss_step=556, train_loss_epoch=573]Epoch 25/400: 6%|▌ | 24/400 [00:04<01:08, 5.52it/s, v_num=1, train_loss_step=556, train_loss_epoch=573]Epoch 25/400: 6%|▋ | 25/400 [00:04<01:08, 5.46it/s, v_num=1, train_loss_step=556, train_loss_epoch=573]Epoch 25/400: 6%|▋ | 25/400 [00:04<01:08, 5.46it/s, v_num=1, train_loss_step=589, train_loss_epoch=573]Epoch 26/400: 6%|▋ | 25/400 [00:04<01:08, 5.46it/s, v_num=1, train_loss_step=589, train_loss_epoch=573]Epoch 26/400: 6%|▋ | 26/400 [00:04<01:07, 5.56it/s, v_num=1, train_loss_step=589, train_loss_epoch=573]Epoch 26/400: 6%|▋ | 26/400 [00:04<01:07, 5.56it/s, v_num=1, train_loss_step=606, train_loss_epoch=572]Epoch 27/400: 6%|▋ | 26/400 [00:04<01:07, 5.56it/s, v_num=1, train_loss_step=606, train_loss_epoch=572]Epoch 27/400: 7%|▋ | 27/400 [00:05<01:08, 5.46it/s, v_num=1, train_loss_step=606, train_loss_epoch=572]Epoch 27/400: 7%|▋ | 27/400 [00:05<01:08, 5.46it/s, v_num=1, train_loss_step=533, train_loss_epoch=572]Epoch 28/400: 7%|▋ | 27/400 [00:05<01:08, 5.46it/s, v_num=1, train_loss_step=533, train_loss_epoch=572]Epoch 28/400: 7%|▋ | 28/400 [00:05<01:10, 5.28it/s, v_num=1, train_loss_step=533, train_loss_epoch=572]Epoch 28/400: 7%|▋ | 28/400 [00:05<01:10, 5.28it/s, v_num=1, train_loss_step=544, train_loss_epoch=571]Epoch 29/400: 7%|▋ | 28/400 [00:05<01:10, 5.28it/s, v_num=1, train_loss_step=544, train_loss_epoch=571]Epoch 29/400: 7%|▋ | 29/400 [00:05<01:11, 5.18it/s, v_num=1, train_loss_step=544, train_loss_epoch=571]Epoch 29/400: 7%|▋ | 29/400 [00:05<01:11, 5.18it/s, v_num=1, train_loss_step=634, train_loss_epoch=570]Epoch 30/400: 7%|▋ | 29/400 [00:05<01:11, 5.18it/s, v_num=1, train_loss_step=634, train_loss_epoch=570]Epoch 30/400: 8%|▊ | 30/400 [00:05<01:11, 5.18it/s, v_num=1, train_loss_step=634, train_loss_epoch=570]Epoch 30/400: 8%|▊ | 30/400 [00:05<01:11, 5.18it/s, v_num=1, train_loss_step=574, train_loss_epoch=569]Epoch 31/400: 8%|▊ | 30/400 [00:05<01:11, 5.18it/s, v_num=1, train_loss_step=574, train_loss_epoch=569]Epoch 31/400: 8%|▊ | 31/400 [00:05<01:10, 5.24it/s, v_num=1, train_loss_step=574, train_loss_epoch=569]Epoch 31/400: 8%|▊ | 31/400 [00:05<01:10, 5.24it/s, v_num=1, train_loss_step=579, train_loss_epoch=569]Epoch 32/400: 8%|▊ | 31/400 [00:05<01:10, 5.24it/s, v_num=1, train_loss_step=579, train_loss_epoch=569]Epoch 32/400: 8%|▊ | 32/400 [00:05<01:06, 5.51it/s, v_num=1, train_loss_step=579, train_loss_epoch=569]Epoch 32/400: 8%|▊ | 32/400 [00:05<01:06, 5.51it/s, v_num=1, train_loss_step=577, train_loss_epoch=568]Epoch 33/400: 8%|▊ | 32/400 [00:05<01:06, 5.51it/s, v_num=1, train_loss_step=577, train_loss_epoch=568]Epoch 33/400: 8%|▊ | 33/400 [00:06<01:04, 5.71it/s, v_num=1, train_loss_step=577, train_loss_epoch=568]Epoch 33/400: 8%|▊ | 33/400 [00:06<01:04, 5.71it/s, v_num=1, train_loss_step=586, train_loss_epoch=567]Epoch 34/400: 8%|▊ | 33/400 [00:06<01:04, 5.71it/s, v_num=1, train_loss_step=586, train_loss_epoch=567]Epoch 34/400: 8%|▊ | 34/400 [00:06<01:02, 5.88it/s, v_num=1, train_loss_step=586, train_loss_epoch=567]Epoch 34/400: 8%|▊ | 34/400 [00:06<01:02, 5.88it/s, v_num=1, train_loss_step=559, train_loss_epoch=567]Epoch 35/400: 8%|▊ | 34/400 [00:06<01:02, 5.88it/s, v_num=1, train_loss_step=559, train_loss_epoch=567]Epoch 35/400: 9%|▉ | 35/400 [00:06<01:00, 6.02it/s, v_num=1, train_loss_step=559, train_loss_epoch=567]Epoch 35/400: 9%|▉ | 35/400 [00:06<01:00, 6.02it/s, v_num=1, train_loss_step=539, train_loss_epoch=566]Epoch 36/400: 9%|▉ | 35/400 [00:06<01:00, 6.02it/s, v_num=1, train_loss_step=539, train_loss_epoch=566]Epoch 36/400: 9%|▉ | 36/400 [00:06<00:59, 6.12it/s, v_num=1, train_loss_step=539, train_loss_epoch=566]Epoch 36/400: 9%|▉ | 36/400 [00:06<00:59, 6.12it/s, v_num=1, train_loss_step=564, train_loss_epoch=566]Epoch 37/400: 9%|▉ | 36/400 [00:06<00:59, 6.12it/s, v_num=1, train_loss_step=564, train_loss_epoch=566]Epoch 37/400: 9%|▉ | 37/400 [00:06<01:00, 6.03it/s, v_num=1, train_loss_step=564, train_loss_epoch=566]Epoch 37/400: 9%|▉ | 37/400 [00:06<01:00, 6.03it/s, v_num=1, train_loss_step=577, train_loss_epoch=565]Epoch 38/400: 9%|▉ | 37/400 [00:06<01:00, 6.03it/s, v_num=1, train_loss_step=577, train_loss_epoch=565]Epoch 38/400: 10%|▉ | 38/400 [00:06<01:00, 6.03it/s, v_num=1, train_loss_step=577, train_loss_epoch=565]Epoch 38/400: 10%|▉ | 38/400 [00:06<01:00, 6.03it/s, v_num=1, train_loss_step=564, train_loss_epoch=564]Epoch 39/400: 10%|▉ | 38/400 [00:06<01:00, 6.03it/s, v_num=1, train_loss_step=564, train_loss_epoch=564]Epoch 39/400: 10%|▉ | 39/400 [00:07<01:00, 6.02it/s, v_num=1, train_loss_step=564, train_loss_epoch=564]Epoch 39/400: 10%|▉ | 39/400 [00:07<01:00, 6.02it/s, v_num=1, train_loss_step=576, train_loss_epoch=564]Epoch 40/400: 10%|▉ | 39/400 [00:07<01:00, 6.02it/s, v_num=1, train_loss_step=576, train_loss_epoch=564]Epoch 40/400: 10%|█ | 40/400 [00:07<01:02, 5.75it/s, v_num=1, train_loss_step=576, train_loss_epoch=564]Epoch 40/400: 10%|█ | 40/400 [00:07<01:02, 5.75it/s, v_num=1, train_loss_step=543, train_loss_epoch=563]Epoch 41/400: 10%|█ | 40/400 [00:07<01:02, 5.75it/s, v_num=1, train_loss_step=543, train_loss_epoch=563]Epoch 41/400: 10%|█ | 41/400 [00:07<01:04, 5.61it/s, v_num=1, train_loss_step=543, train_loss_epoch=563]Epoch 41/400: 10%|█ | 41/400 [00:07<01:04, 5.61it/s, v_num=1, train_loss_step=541, train_loss_epoch=563]Epoch 42/400: 10%|█ | 41/400 [00:07<01:04, 5.61it/s, v_num=1, train_loss_step=541, train_loss_epoch=563]Epoch 42/400: 10%|█ | 42/400 [00:07<01:05, 5.45it/s, v_num=1, train_loss_step=541, train_loss_epoch=563]Epoch 42/400: 10%|█ | 42/400 [00:07<01:05, 5.45it/s, v_num=1, train_loss_step=592, train_loss_epoch=563]Epoch 43/400: 10%|█ | 42/400 [00:07<01:05, 5.45it/s, v_num=1, train_loss_step=592, train_loss_epoch=563]Epoch 43/400: 11%|█ | 43/400 [00:07<01:04, 5.53it/s, v_num=1, train_loss_step=592, train_loss_epoch=563]Epoch 43/400: 11%|█ | 43/400 [00:07<01:04, 5.53it/s, v_num=1, train_loss_step=559, train_loss_epoch=562]Epoch 44/400: 11%|█ | 43/400 [00:07<01:04, 5.53it/s, v_num=1, train_loss_step=559, train_loss_epoch=562]Epoch 44/400: 11%|█ | 44/400 [00:08<01:02, 5.68it/s, v_num=1, train_loss_step=559, train_loss_epoch=562]Epoch 44/400: 11%|█ | 44/400 [00:08<01:02, 5.68it/s, v_num=1, train_loss_step=571, train_loss_epoch=561]Epoch 45/400: 11%|█ | 44/400 [00:08<01:02, 5.68it/s, v_num=1, train_loss_step=571, train_loss_epoch=561]Epoch 45/400: 11%|█▏ | 45/400 [00:08<01:01, 5.80it/s, v_num=1, train_loss_step=571, train_loss_epoch=561]Epoch 45/400: 11%|█▏ | 45/400 [00:08<01:01, 5.80it/s, v_num=1, train_loss_step=585, train_loss_epoch=561]Epoch 46/400: 11%|█▏ | 45/400 [00:08<01:01, 5.80it/s, v_num=1, train_loss_step=585, train_loss_epoch=561]Epoch 46/400: 12%|█▏ | 46/400 [00:08<00:59, 5.91it/s, v_num=1, train_loss_step=585, train_loss_epoch=561]Epoch 46/400: 12%|█▏ | 46/400 [00:08<00:59, 5.91it/s, v_num=1, train_loss_step=571, train_loss_epoch=560]Epoch 47/400: 12%|█▏ | 46/400 [00:08<00:59, 5.91it/s, v_num=1, train_loss_step=571, train_loss_epoch=560]Epoch 47/400: 12%|█▏ | 47/400 [00:08<00:59, 5.89it/s, v_num=1, train_loss_step=571, train_loss_epoch=560]Epoch 47/400: 12%|█▏ | 47/400 [00:08<00:59, 5.89it/s, v_num=1, train_loss_step=556, train_loss_epoch=560]Epoch 48/400: 12%|█▏ | 47/400 [00:08<00:59, 5.89it/s, v_num=1, train_loss_step=556, train_loss_epoch=560]Epoch 48/400: 12%|█▏ | 48/400 [00:08<00:59, 5.93it/s, v_num=1, train_loss_step=556, train_loss_epoch=560]Epoch 48/400: 12%|█▏ | 48/400 [00:08<00:59, 5.93it/s, v_num=1, train_loss_step=562, train_loss_epoch=560]Epoch 49/400: 12%|█▏ | 48/400 [00:08<00:59, 5.93it/s, v_num=1, train_loss_step=562, train_loss_epoch=560]Epoch 49/400: 12%|█▏ | 49/400 [00:08<00:58, 6.00it/s, v_num=1, train_loss_step=562, train_loss_epoch=560]Epoch 49/400: 12%|█▏ | 49/400 [00:08<00:58, 6.00it/s, v_num=1, train_loss_step=565, train_loss_epoch=559]Epoch 50/400: 12%|█▏ | 49/400 [00:08<00:58, 6.00it/s, v_num=1, train_loss_step=565, train_loss_epoch=559]Epoch 50/400: 12%|█▎ | 50/400 [00:09<01:00, 5.77it/s, v_num=1, train_loss_step=565, train_loss_epoch=559]Epoch 50/400: 12%|█▎ | 50/400 [00:09<01:00, 5.77it/s, v_num=1, train_loss_step=594, train_loss_epoch=559]Epoch 51/400: 12%|█▎ | 50/400 [00:09<01:00, 5.77it/s, v_num=1, train_loss_step=594, train_loss_epoch=559]Epoch 51/400: 13%|█▎ | 51/400 [00:09<01:00, 5.74it/s, v_num=1, train_loss_step=594, train_loss_epoch=559]Epoch 51/400: 13%|█▎ | 51/400 [00:09<01:00, 5.74it/s, v_num=1, train_loss_step=548, train_loss_epoch=558]Epoch 52/400: 13%|█▎ | 51/400 [00:09<01:00, 5.74it/s, v_num=1, train_loss_step=548, train_loss_epoch=558]Epoch 52/400: 13%|█▎ | 52/400 [00:09<01:01, 5.66it/s, v_num=1, train_loss_step=548, train_loss_epoch=558]Epoch 52/400: 13%|█▎ | 52/400 [00:09<01:01, 5.66it/s, v_num=1, train_loss_step=567, train_loss_epoch=558]Epoch 53/400: 13%|█▎ | 52/400 [00:09<01:01, 5.66it/s, v_num=1, train_loss_step=567, train_loss_epoch=558]Epoch 53/400: 13%|█▎ | 53/400 [00:09<01:02, 5.58it/s, v_num=1, train_loss_step=567, train_loss_epoch=558]Epoch 53/400: 13%|█▎ | 53/400 [00:09<01:02, 5.58it/s, v_num=1, train_loss_step=559, train_loss_epoch=558]Epoch 54/400: 13%|█▎ | 53/400 [00:09<01:02, 5.58it/s, v_num=1, train_loss_step=559, train_loss_epoch=558]Epoch 54/400: 14%|█▎ | 54/400 [00:09<01:02, 5.54it/s, v_num=1, train_loss_step=559, train_loss_epoch=558]Epoch 54/400: 14%|█▎ | 54/400 [00:09<01:02, 5.54it/s, v_num=1, train_loss_step=536, train_loss_epoch=557]Epoch 55/400: 14%|█▎ | 54/400 [00:09<01:02, 5.54it/s, v_num=1, train_loss_step=536, train_loss_epoch=557]Epoch 55/400: 14%|█▍ | 55/400 [00:09<01:00, 5.75it/s, v_num=1, train_loss_step=536, train_loss_epoch=557]Epoch 55/400: 14%|█▍ | 55/400 [00:09<01:00, 5.75it/s, v_num=1, train_loss_step=535, train_loss_epoch=557]Epoch 56/400: 14%|█▍ | 55/400 [00:09<01:00, 5.75it/s, v_num=1, train_loss_step=535, train_loss_epoch=557]Epoch 56/400: 14%|█▍ | 56/400 [00:10<00:59, 5.77it/s, v_num=1, train_loss_step=535, train_loss_epoch=557]Epoch 56/400: 14%|█▍ | 56/400 [00:10<00:59, 5.77it/s, v_num=1, train_loss_step=551, train_loss_epoch=556]Epoch 57/400: 14%|█▍ | 56/400 [00:10<00:59, 5.77it/s, v_num=1, train_loss_step=551, train_loss_epoch=556]Epoch 57/400: 14%|█▍ | 57/400 [00:10<00:59, 5.81it/s, v_num=1, train_loss_step=551, train_loss_epoch=556]Epoch 57/400: 14%|█▍ | 57/400 [00:10<00:59, 5.81it/s, v_num=1, train_loss_step=556, train_loss_epoch=556]Epoch 58/400: 14%|█▍ | 57/400 [00:10<00:59, 5.81it/s, v_num=1, train_loss_step=556, train_loss_epoch=556]Epoch 58/400: 14%|█▍ | 58/400 [00:10<01:00, 5.70it/s, v_num=1, train_loss_step=556, train_loss_epoch=556]Epoch 58/400: 14%|█▍ | 58/400 [00:10<01:00, 5.70it/s, v_num=1, train_loss_step=573, train_loss_epoch=555]Epoch 59/400: 14%|█▍ | 58/400 [00:10<01:00, 5.70it/s, v_num=1, train_loss_step=573, train_loss_epoch=555]Epoch 59/400: 15%|█▍ | 59/400 [00:10<01:02, 5.43it/s, v_num=1, train_loss_step=573, train_loss_epoch=555]Epoch 59/400: 15%|█▍ | 59/400 [00:10<01:02, 5.43it/s, v_num=1, train_loss_step=541, train_loss_epoch=555]Epoch 60/400: 15%|█▍ | 59/400 [00:10<01:02, 5.43it/s, v_num=1, train_loss_step=541, train_loss_epoch=555]Epoch 60/400: 15%|█▌ | 60/400 [00:10<01:03, 5.33it/s, v_num=1, train_loss_step=541, train_loss_epoch=555]Epoch 60/400: 15%|█▌ | 60/400 [00:10<01:03, 5.33it/s, v_num=1, train_loss_step=565, train_loss_epoch=554]Epoch 61/400: 15%|█▌ | 60/400 [00:10<01:03, 5.33it/s, v_num=1, train_loss_step=565, train_loss_epoch=554]Epoch 61/400: 15%|█▌ | 61/400 [00:11<01:01, 5.47it/s, v_num=1, train_loss_step=565, train_loss_epoch=554]Epoch 61/400: 15%|█▌ | 61/400 [00:11<01:01, 5.47it/s, v_num=1, train_loss_step=568, train_loss_epoch=554]Epoch 62/400: 15%|█▌ | 61/400 [00:11<01:01, 5.47it/s, v_num=1, train_loss_step=568, train_loss_epoch=554]Epoch 62/400: 16%|█▌ | 62/400 [00:11<01:00, 5.63it/s, v_num=1, train_loss_step=568, train_loss_epoch=554]Epoch 62/400: 16%|█▌ | 62/400 [00:11<01:00, 5.63it/s, v_num=1, train_loss_step=556, train_loss_epoch=554]Epoch 63/400: 16%|█▌ | 62/400 [00:11<01:00, 5.63it/s, v_num=1, train_loss_step=556, train_loss_epoch=554]Epoch 63/400: 16%|█▌ | 63/400 [00:11<01:00, 5.56it/s, v_num=1, train_loss_step=556, train_loss_epoch=554]Epoch 63/400: 16%|█▌ | 63/400 [00:11<01:00, 5.56it/s, v_num=1, train_loss_step=545, train_loss_epoch=554]Epoch 64/400: 16%|█▌ | 63/400 [00:11<01:00, 5.56it/s, v_num=1, train_loss_step=545, train_loss_epoch=554]Epoch 64/400: 16%|█▌ | 64/400 [00:11<00:59, 5.62it/s, v_num=1, train_loss_step=545, train_loss_epoch=554]Epoch 64/400: 16%|█▌ | 64/400 [00:11<00:59, 5.62it/s, v_num=1, train_loss_step=577, train_loss_epoch=553]Epoch 65/400: 16%|█▌ | 64/400 [00:11<00:59, 5.62it/s, v_num=1, train_loss_step=577, train_loss_epoch=553]Epoch 65/400: 16%|█▋ | 65/400 [00:11<00:58, 5.70it/s, v_num=1, train_loss_step=577, train_loss_epoch=553]Epoch 65/400: 16%|█▋ | 65/400 [00:11<00:58, 5.70it/s, v_num=1, train_loss_step=536, train_loss_epoch=553]Epoch 66/400: 16%|█▋ | 65/400 [00:11<00:58, 5.70it/s, v_num=1, train_loss_step=536, train_loss_epoch=553]Epoch 66/400: 16%|█▋ | 66/400 [00:11<00:58, 5.74it/s, v_num=1, train_loss_step=536, train_loss_epoch=553]Epoch 66/400: 16%|█▋ | 66/400 [00:11<00:58, 5.74it/s, v_num=1, train_loss_step=553, train_loss_epoch=553]Epoch 67/400: 16%|█▋ | 66/400 [00:11<00:58, 5.74it/s, v_num=1, train_loss_step=553, train_loss_epoch=553]Epoch 67/400: 17%|█▋ | 67/400 [00:12<00:57, 5.83it/s, v_num=1, train_loss_step=553, train_loss_epoch=553]Epoch 67/400: 17%|█▋ | 67/400 [00:12<00:57, 5.83it/s, v_num=1, train_loss_step=605, train_loss_epoch=552]Epoch 68/400: 17%|█▋ | 67/400 [00:12<00:57, 5.83it/s, v_num=1, train_loss_step=605, train_loss_epoch=552]Epoch 68/400: 17%|█▋ | 68/400 [00:12<00:59, 5.61it/s, v_num=1, train_loss_step=605, train_loss_epoch=552]Epoch 68/400: 17%|█▋ | 68/400 [00:12<00:59, 5.61it/s, v_num=1, train_loss_step=556, train_loss_epoch=552]Epoch 69/400: 17%|█▋ | 68/400 [00:12<00:59, 5.61it/s, v_num=1, train_loss_step=556, train_loss_epoch=552]Epoch 69/400: 17%|█▋ | 69/400 [00:12<00:59, 5.56it/s, v_num=1, train_loss_step=556, train_loss_epoch=552]Epoch 69/400: 17%|█▋ | 69/400 [00:12<00:59, 5.56it/s, v_num=1, train_loss_step=525, train_loss_epoch=551]Epoch 70/400: 17%|█▋ | 69/400 [00:12<00:59, 5.56it/s, v_num=1, train_loss_step=525, train_loss_epoch=551]Epoch 70/400: 18%|█▊ | 70/400 [00:12<01:00, 5.47it/s, v_num=1, train_loss_step=525, train_loss_epoch=551]Epoch 70/400: 18%|█▊ | 70/400 [00:12<01:00, 5.47it/s, v_num=1, train_loss_step=566, train_loss_epoch=551]Epoch 71/400: 18%|█▊ | 70/400 [00:12<01:00, 5.47it/s, v_num=1, train_loss_step=566, train_loss_epoch=551]Epoch 71/400: 18%|█▊ | 71/400 [00:12<00:57, 5.68it/s, v_num=1, train_loss_step=566, train_loss_epoch=551]Epoch 71/400: 18%|█▊ | 71/400 [00:12<00:57, 5.68it/s, v_num=1, train_loss_step=544, train_loss_epoch=551]Epoch 72/400: 18%|█▊ | 71/400 [00:12<00:57, 5.68it/s, v_num=1, train_loss_step=544, train_loss_epoch=551]Epoch 72/400: 18%|█▊ | 72/400 [00:12<00:55, 5.87it/s, v_num=1, train_loss_step=544, train_loss_epoch=551]Epoch 72/400: 18%|█▊ | 72/400 [00:12<00:55, 5.87it/s, v_num=1, train_loss_step=573, train_loss_epoch=551]Epoch 73/400: 18%|█▊ | 72/400 [00:12<00:55, 5.87it/s, v_num=1, train_loss_step=573, train_loss_epoch=551]Epoch 73/400: 18%|█▊ | 73/400 [00:13<00:54, 6.02it/s, v_num=1, train_loss_step=573, train_loss_epoch=551]Epoch 73/400: 18%|█▊ | 73/400 [00:13<00:54, 6.02it/s, v_num=1, train_loss_step=550, train_loss_epoch=550]Epoch 74/400: 18%|█▊ | 73/400 [00:13<00:54, 6.02it/s, v_num=1, train_loss_step=550, train_loss_epoch=550]Epoch 74/400: 18%|█▊ | 74/400 [00:13<00:53, 6.08it/s, v_num=1, train_loss_step=550, train_loss_epoch=550]Epoch 74/400: 18%|█▊ | 74/400 [00:13<00:53, 6.08it/s, v_num=1, train_loss_step=542, train_loss_epoch=550]Epoch 75/400: 18%|█▊ | 74/400 [00:13<00:53, 6.08it/s, v_num=1, train_loss_step=542, train_loss_epoch=550]Epoch 75/400: 19%|█▉ | 75/400 [00:13<00:54, 5.93it/s, v_num=1, train_loss_step=542, train_loss_epoch=550]Epoch 75/400: 19%|█▉ | 75/400 [00:13<00:54, 5.93it/s, v_num=1, train_loss_step=537, train_loss_epoch=550]Epoch 76/400: 19%|█▉ | 75/400 [00:13<00:54, 5.93it/s, v_num=1, train_loss_step=537, train_loss_epoch=550]Epoch 76/400: 19%|█▉ | 76/400 [00:13<00:54, 5.92it/s, v_num=1, train_loss_step=537, train_loss_epoch=550]Epoch 76/400: 19%|█▉ | 76/400 [00:13<00:54, 5.92it/s, v_num=1, train_loss_step=521, train_loss_epoch=549]Epoch 77/400: 19%|█▉ | 76/400 [00:13<00:54, 5.92it/s, v_num=1, train_loss_step=521, train_loss_epoch=549]Epoch 77/400: 19%|█▉ | 77/400 [00:13<00:54, 5.96it/s, v_num=1, train_loss_step=521, train_loss_epoch=549]Epoch 77/400: 19%|█▉ | 77/400 [00:13<00:54, 5.96it/s, v_num=1, train_loss_step=572, train_loss_epoch=549]Epoch 78/400: 19%|█▉ | 77/400 [00:13<00:54, 5.96it/s, v_num=1, train_loss_step=572, train_loss_epoch=549]Epoch 78/400: 20%|█▉ | 78/400 [00:13<00:52, 6.11it/s, v_num=1, train_loss_step=572, train_loss_epoch=549]Epoch 78/400: 20%|█▉ | 78/400 [00:13<00:52, 6.11it/s, v_num=1, train_loss_step=556, train_loss_epoch=548]Epoch 79/400: 20%|█▉ | 78/400 [00:13<00:52, 6.11it/s, v_num=1, train_loss_step=556, train_loss_epoch=548]Epoch 79/400: 20%|█▉ | 79/400 [00:14<00:51, 6.18it/s, v_num=1, train_loss_step=556, train_loss_epoch=548]Epoch 79/400: 20%|█▉ | 79/400 [00:14<00:51, 6.18it/s, v_num=1, train_loss_step=541, train_loss_epoch=548]Epoch 80/400: 20%|█▉ | 79/400 [00:14<00:51, 6.18it/s, v_num=1, train_loss_step=541, train_loss_epoch=548]Epoch 80/400: 20%|██ | 80/400 [00:14<00:51, 6.20it/s, v_num=1, train_loss_step=541, train_loss_epoch=548]Epoch 80/400: 20%|██ | 80/400 [00:14<00:51, 6.20it/s, v_num=1, train_loss_step=564, train_loss_epoch=548]Epoch 81/400: 20%|██ | 80/400 [00:14<00:51, 6.20it/s, v_num=1, train_loss_step=564, train_loss_epoch=548]Epoch 81/400: 20%|██ | 81/400 [00:14<00:51, 6.14it/s, v_num=1, train_loss_step=564, train_loss_epoch=548]Epoch 81/400: 20%|██ | 81/400 [00:14<00:51, 6.14it/s, v_num=1, train_loss_step=534, train_loss_epoch=548]Epoch 82/400: 20%|██ | 81/400 [00:14<00:51, 6.14it/s, v_num=1, train_loss_step=534, train_loss_epoch=548]Epoch 82/400: 20%|██ | 82/400 [00:14<00:51, 6.13it/s, v_num=1, train_loss_step=534, train_loss_epoch=548]Epoch 82/400: 20%|██ | 82/400 [00:14<00:51, 6.13it/s, v_num=1, train_loss_step=558, train_loss_epoch=547]Epoch 83/400: 20%|██ | 82/400 [00:14<00:51, 6.13it/s, v_num=1, train_loss_step=558, train_loss_epoch=547]Epoch 83/400: 21%|██ | 83/400 [00:14<00:52, 6.06it/s, v_num=1, train_loss_step=558, train_loss_epoch=547]Epoch 83/400: 21%|██ | 83/400 [00:14<00:52, 6.06it/s, v_num=1, train_loss_step=543, train_loss_epoch=547]Epoch 84/400: 21%|██ | 83/400 [00:14<00:52, 6.06it/s, v_num=1, train_loss_step=543, train_loss_epoch=547]Epoch 84/400: 21%|██ | 84/400 [00:14<00:53, 5.95it/s, v_num=1, train_loss_step=543, train_loss_epoch=547]Epoch 84/400: 21%|██ | 84/400 [00:14<00:53, 5.95it/s, v_num=1, train_loss_step=529, train_loss_epoch=547]Epoch 85/400: 21%|██ | 84/400 [00:14<00:53, 5.95it/s, v_num=1, train_loss_step=529, train_loss_epoch=547]Epoch 85/400: 21%|██▏ | 85/400 [00:15<00:54, 5.81it/s, v_num=1, train_loss_step=529, train_loss_epoch=547]Epoch 85/400: 21%|██▏ | 85/400 [00:15<00:54, 5.81it/s, v_num=1, train_loss_step=532, train_loss_epoch=547]Epoch 86/400: 21%|██▏ | 85/400 [00:15<00:54, 5.81it/s, v_num=1, train_loss_step=532, train_loss_epoch=547]Epoch 86/400: 22%|██▏ | 86/400 [00:15<00:55, 5.64it/s, v_num=1, train_loss_step=532, train_loss_epoch=547]Epoch 86/400: 22%|██▏ | 86/400 [00:15<00:55, 5.64it/s, v_num=1, train_loss_step=524, train_loss_epoch=546]Epoch 87/400: 22%|██▏ | 86/400 [00:15<00:55, 5.64it/s, v_num=1, train_loss_step=524, train_loss_epoch=546]Epoch 87/400: 22%|██▏ | 87/400 [00:15<00:55, 5.61it/s, v_num=1, train_loss_step=524, train_loss_epoch=546]Epoch 87/400: 22%|██▏ | 87/400 [00:15<00:55, 5.61it/s, v_num=1, train_loss_step=580, train_loss_epoch=546]Epoch 88/400: 22%|██▏ | 87/400 [00:15<00:55, 5.61it/s, v_num=1, train_loss_step=580, train_loss_epoch=546]Epoch 88/400: 22%|██▏ | 88/400 [00:15<00:53, 5.78it/s, v_num=1, train_loss_step=580, train_loss_epoch=546]Epoch 88/400: 22%|██▏ | 88/400 [00:15<00:53, 5.78it/s, v_num=1, train_loss_step=565, train_loss_epoch=545]Epoch 89/400: 22%|██▏ | 88/400 [00:15<00:53, 5.78it/s, v_num=1, train_loss_step=565, train_loss_epoch=545]Epoch 89/400: 22%|██▏ | 89/400 [00:15<00:52, 5.89it/s, v_num=1, train_loss_step=565, train_loss_epoch=545]Epoch 89/400: 22%|██▏ | 89/400 [00:15<00:52, 5.89it/s, v_num=1, train_loss_step=569, train_loss_epoch=545]Epoch 90/400: 22%|██▏ | 89/400 [00:15<00:52, 5.89it/s, v_num=1, train_loss_step=569, train_loss_epoch=545]Epoch 90/400: 22%|██▎ | 90/400 [00:15<00:51, 5.99it/s, v_num=1, train_loss_step=569, train_loss_epoch=545]Epoch 90/400: 22%|██▎ | 90/400 [00:15<00:51, 5.99it/s, v_num=1, train_loss_step=551, train_loss_epoch=545]Epoch 91/400: 22%|██▎ | 90/400 [00:15<00:51, 5.99it/s, v_num=1, train_loss_step=551, train_loss_epoch=545]Epoch 91/400: 23%|██▎ | 91/400 [00:16<00:50, 6.09it/s, v_num=1, train_loss_step=551, train_loss_epoch=545]Epoch 91/400: 23%|██▎ | 91/400 [00:16<00:50, 6.09it/s, v_num=1, train_loss_step=532, train_loss_epoch=545]Epoch 92/400: 23%|██▎ | 91/400 [00:16<00:50, 6.09it/s, v_num=1, train_loss_step=532, train_loss_epoch=545]Epoch 92/400: 23%|██▎ | 92/400 [00:16<00:51, 6.00it/s, v_num=1, train_loss_step=532, train_loss_epoch=545]Epoch 92/400: 23%|██▎ | 92/400 [00:16<00:51, 6.00it/s, v_num=1, train_loss_step=529, train_loss_epoch=544]Epoch 93/400: 23%|██▎ | 92/400 [00:16<00:51, 6.00it/s, v_num=1, train_loss_step=529, train_loss_epoch=544]Epoch 93/400: 23%|██▎ | 93/400 [00:16<00:51, 6.00it/s, v_num=1, train_loss_step=529, train_loss_epoch=544]Epoch 93/400: 23%|██▎ | 93/400 [00:16<00:51, 6.00it/s, v_num=1, train_loss_step=517, train_loss_epoch=544]Epoch 94/400: 23%|██▎ | 93/400 [00:16<00:51, 6.00it/s, v_num=1, train_loss_step=517, train_loss_epoch=544]Epoch 94/400: 24%|██▎ | 94/400 [00:16<00:51, 5.99it/s, v_num=1, train_loss_step=517, train_loss_epoch=544]Epoch 94/400: 24%|██▎ | 94/400 [00:16<00:51, 5.99it/s, v_num=1, train_loss_step=558, train_loss_epoch=544]Epoch 95/400: 24%|██▎ | 94/400 [00:16<00:51, 5.99it/s, v_num=1, train_loss_step=558, train_loss_epoch=544]Epoch 95/400: 24%|██▍ | 95/400 [00:16<00:50, 6.05it/s, v_num=1, train_loss_step=558, train_loss_epoch=544]Epoch 95/400: 24%|██▍ | 95/400 [00:16<00:50, 6.05it/s, v_num=1, train_loss_step=542, train_loss_epoch=544]Epoch 96/400: 24%|██▍ | 95/400 [00:16<00:50, 6.05it/s, v_num=1, train_loss_step=542, train_loss_epoch=544]Epoch 96/400: 24%|██▍ | 96/400 [00:16<00:50, 6.08it/s, v_num=1, train_loss_step=542, train_loss_epoch=544]Epoch 96/400: 24%|██▍ | 96/400 [00:16<00:50, 6.08it/s, v_num=1, train_loss_step=535, train_loss_epoch=543]Epoch 97/400: 24%|██▍ | 96/400 [00:16<00:50, 6.08it/s, v_num=1, train_loss_step=535, train_loss_epoch=543]Epoch 97/400: 24%|██▍ | 97/400 [00:17<00:51, 5.89it/s, v_num=1, train_loss_step=535, train_loss_epoch=543]Epoch 97/400: 24%|██▍ | 97/400 [00:17<00:51, 5.89it/s, v_num=1, train_loss_step=525, train_loss_epoch=543]Epoch 98/400: 24%|██▍ | 97/400 [00:17<00:51, 5.89it/s, v_num=1, train_loss_step=525, train_loss_epoch=543]Epoch 98/400: 24%|██▍ | 98/400 [00:17<00:51, 5.90it/s, v_num=1, train_loss_step=525, train_loss_epoch=543]Epoch 98/400: 24%|██▍ | 98/400 [00:17<00:51, 5.90it/s, v_num=1, train_loss_step=564, train_loss_epoch=542]Epoch 99/400: 24%|██▍ | 98/400 [00:17<00:51, 5.90it/s, v_num=1, train_loss_step=564, train_loss_epoch=542]Epoch 99/400: 25%|██▍ | 99/400 [00:17<00:51, 5.80it/s, v_num=1, train_loss_step=564, train_loss_epoch=542]Epoch 99/400: 25%|██▍ | 99/400 [00:17<00:51, 5.80it/s, v_num=1, train_loss_step=549, train_loss_epoch=542]Epoch 100/400: 25%|██▍ | 99/400 [00:17<00:51, 5.80it/s, v_num=1, train_loss_step=549, train_loss_epoch=542]Epoch 100/400: 25%|██▌ | 100/400 [00:17<00:51, 5.79it/s, v_num=1, train_loss_step=549, train_loss_epoch=542]Epoch 100/400: 25%|██▌ | 100/400 [00:17<00:51, 5.79it/s, v_num=1, train_loss_step=542, train_loss_epoch=542]Epoch 101/400: 25%|██▌ | 100/400 [00:17<00:51, 5.79it/s, v_num=1, train_loss_step=542, train_loss_epoch=542]Epoch 101/400: 25%|██▌ | 101/400 [00:17<00:53, 5.64it/s, v_num=1, train_loss_step=542, train_loss_epoch=542]Epoch 101/400: 25%|██▌ | 101/400 [00:17<00:53, 5.64it/s, v_num=1, train_loss_step=537, train_loss_epoch=542]Epoch 102/400: 25%|██▌ | 101/400 [00:17<00:53, 5.64it/s, v_num=1, train_loss_step=537, train_loss_epoch=542]Epoch 102/400: 26%|██▌ | 102/400 [00:18<00:52, 5.67it/s, v_num=1, train_loss_step=537, train_loss_epoch=542]Epoch 102/400: 26%|██▌ | 102/400 [00:18<00:52, 5.67it/s, v_num=1, train_loss_step=537, train_loss_epoch=541]Epoch 103/400: 26%|██▌ | 102/400 [00:18<00:52, 5.67it/s, v_num=1, train_loss_step=537, train_loss_epoch=541]Epoch 103/400: 26%|██▌ | 103/400 [00:18<00:51, 5.75it/s, v_num=1, train_loss_step=537, train_loss_epoch=541]Epoch 103/400: 26%|██▌ | 103/400 [00:18<00:51, 5.75it/s, v_num=1, train_loss_step=556, train_loss_epoch=542]Epoch 104/400: 26%|██▌ | 103/400 [00:18<00:51, 5.75it/s, v_num=1, train_loss_step=556, train_loss_epoch=542]Epoch 104/400: 26%|██▌ | 104/400 [00:18<00:52, 5.61it/s, v_num=1, train_loss_step=556, train_loss_epoch=542]Epoch 104/400: 26%|██▌ | 104/400 [00:18<00:52, 5.61it/s, v_num=1, train_loss_step=557, train_loss_epoch=541]Epoch 105/400: 26%|██▌ | 104/400 [00:18<00:52, 5.61it/s, v_num=1, train_loss_step=557, train_loss_epoch=541]Epoch 105/400: 26%|██▋ | 105/400 [00:18<00:51, 5.70it/s, v_num=1, train_loss_step=557, train_loss_epoch=541]Epoch 105/400: 26%|██▋ | 105/400 [00:18<00:51, 5.70it/s, v_num=1, train_loss_step=523, train_loss_epoch=541]Epoch 106/400: 26%|██▋ | 105/400 [00:18<00:51, 5.70it/s, v_num=1, train_loss_step=523, train_loss_epoch=541]Epoch 106/400: 26%|██▋ | 106/400 [00:18<00:50, 5.77it/s, v_num=1, train_loss_step=523, train_loss_epoch=541]Epoch 106/400: 26%|██▋ | 106/400 [00:18<00:50, 5.77it/s, v_num=1, train_loss_step=537, train_loss_epoch=540]Epoch 107/400: 26%|██▋ | 106/400 [00:18<00:50, 5.77it/s, v_num=1, train_loss_step=537, train_loss_epoch=540]Epoch 107/400: 27%|██▋ | 107/400 [00:18<00:52, 5.59it/s, v_num=1, train_loss_step=537, train_loss_epoch=540]Epoch 107/400: 27%|██▋ | 107/400 [00:18<00:52, 5.59it/s, v_num=1, train_loss_step=548, train_loss_epoch=541]Epoch 108/400: 27%|██▋ | 107/400 [00:18<00:52, 5.59it/s, v_num=1, train_loss_step=548, train_loss_epoch=541]Epoch 108/400: 27%|██▋ | 108/400 [00:19<00:52, 5.58it/s, v_num=1, train_loss_step=548, train_loss_epoch=541]Epoch 108/400: 27%|██▋ | 108/400 [00:19<00:52, 5.58it/s, v_num=1, train_loss_step=532, train_loss_epoch=540]Epoch 109/400: 27%|██▋ | 108/400 [00:19<00:52, 5.58it/s, v_num=1, train_loss_step=532, train_loss_epoch=540]Epoch 109/400: 27%|██▋ | 109/400 [00:19<00:53, 5.44it/s, v_num=1, train_loss_step=532, train_loss_epoch=540]Epoch 109/400: 27%|██▋ | 109/400 [00:19<00:53, 5.44it/s, v_num=1, train_loss_step=557, train_loss_epoch=539]Epoch 110/400: 27%|██▋ | 109/400 [00:19<00:53, 5.44it/s, v_num=1, train_loss_step=557, train_loss_epoch=539]Epoch 110/400: 28%|██▊ | 110/400 [00:19<00:52, 5.52it/s, v_num=1, train_loss_step=557, train_loss_epoch=539]Epoch 110/400: 28%|██▊ | 110/400 [00:19<00:52, 5.52it/s, v_num=1, train_loss_step=572, train_loss_epoch=539]Epoch 111/400: 28%|██▊ | 110/400 [00:19<00:52, 5.52it/s, v_num=1, train_loss_step=572, train_loss_epoch=539]Epoch 111/400: 28%|██▊ | 111/400 [00:19<00:52, 5.51it/s, v_num=1, train_loss_step=572, train_loss_epoch=539]Epoch 111/400: 28%|██▊ | 111/400 [00:19<00:52, 5.51it/s, v_num=1, train_loss_step=575, train_loss_epoch=539]Epoch 112/400: 28%|██▊ | 111/400 [00:19<00:52, 5.51it/s, v_num=1, train_loss_step=575, train_loss_epoch=539]Epoch 112/400: 28%|██▊ | 112/400 [00:19<00:52, 5.53it/s, v_num=1, train_loss_step=575, train_loss_epoch=539]Epoch 112/400: 28%|██▊ | 112/400 [00:19<00:52, 5.53it/s, v_num=1, train_loss_step=522, train_loss_epoch=539]Epoch 113/400: 28%|██▊ | 112/400 [00:19<00:52, 5.53it/s, v_num=1, train_loss_step=522, train_loss_epoch=539]Epoch 113/400: 28%|██▊ | 113/400 [00:19<00:52, 5.49it/s, v_num=1, train_loss_step=522, train_loss_epoch=539]Epoch 113/400: 28%|██▊ | 113/400 [00:19<00:52, 5.49it/s, v_num=1, train_loss_step=574, train_loss_epoch=539]Epoch 114/400: 28%|██▊ | 113/400 [00:19<00:52, 5.49it/s, v_num=1, train_loss_step=574, train_loss_epoch=539]Epoch 114/400: 28%|██▊ | 114/400 [00:20<00:52, 5.48it/s, v_num=1, train_loss_step=574, train_loss_epoch=539]Epoch 114/400: 28%|██▊ | 114/400 [00:20<00:52, 5.48it/s, v_num=1, train_loss_step=566, train_loss_epoch=539]Epoch 115/400: 28%|██▊ | 114/400 [00:20<00:52, 5.48it/s, v_num=1, train_loss_step=566, train_loss_epoch=539]Epoch 115/400: 29%|██▉ | 115/400 [00:20<00:53, 5.33it/s, v_num=1, train_loss_step=566, train_loss_epoch=539]Epoch 115/400: 29%|██▉ | 115/400 [00:20<00:53, 5.33it/s, v_num=1, train_loss_step=566, train_loss_epoch=538]Epoch 116/400: 29%|██▉ | 115/400 [00:20<00:53, 5.33it/s, v_num=1, train_loss_step=566, train_loss_epoch=538]Epoch 116/400: 29%|██▉ | 116/400 [00:20<00:52, 5.43it/s, v_num=1, train_loss_step=566, train_loss_epoch=538]Epoch 116/400: 29%|██▉ | 116/400 [00:20<00:52, 5.43it/s, v_num=1, train_loss_step=514, train_loss_epoch=538]Epoch 117/400: 29%|██▉ | 116/400 [00:20<00:52, 5.43it/s, v_num=1, train_loss_step=514, train_loss_epoch=538]Epoch 117/400: 29%|██▉ | 117/400 [00:20<00:53, 5.26it/s, v_num=1, train_loss_step=514, train_loss_epoch=538]Epoch 117/400: 29%|██▉ | 117/400 [00:20<00:53, 5.26it/s, v_num=1, train_loss_step=566, train_loss_epoch=538]Epoch 118/400: 29%|██▉ | 117/400 [00:20<00:53, 5.26it/s, v_num=1, train_loss_step=566, train_loss_epoch=538]Epoch 118/400: 30%|██▉ | 118/400 [00:20<00:55, 5.11it/s, v_num=1, train_loss_step=566, train_loss_epoch=538]Epoch 118/400: 30%|██▉ | 118/400 [00:20<00:55, 5.11it/s, v_num=1, train_loss_step=582, train_loss_epoch=537]Epoch 119/400: 30%|██▉ | 118/400 [00:20<00:55, 5.11it/s, v_num=1, train_loss_step=582, train_loss_epoch=537]Epoch 119/400: 30%|██▉ | 119/400 [00:21<00:55, 5.10it/s, v_num=1, train_loss_step=582, train_loss_epoch=537]Epoch 119/400: 30%|██▉ | 119/400 [00:21<00:55, 5.10it/s, v_num=1, train_loss_step=581, train_loss_epoch=538]Epoch 120/400: 30%|██▉ | 119/400 [00:21<00:55, 5.10it/s, v_num=1, train_loss_step=581, train_loss_epoch=538]Epoch 120/400: 30%|███ | 120/400 [00:21<00:54, 5.09it/s, v_num=1, train_loss_step=581, train_loss_epoch=538]Epoch 120/400: 30%|███ | 120/400 [00:21<00:54, 5.09it/s, v_num=1, train_loss_step=549, train_loss_epoch=537]Epoch 121/400: 30%|███ | 120/400 [00:21<00:54, 5.09it/s, v_num=1, train_loss_step=549, train_loss_epoch=537]Epoch 121/400: 30%|███ | 121/400 [00:21<00:51, 5.37it/s, v_num=1, train_loss_step=549, train_loss_epoch=537]Epoch 121/400: 30%|███ | 121/400 [00:21<00:51, 5.37it/s, v_num=1, train_loss_step=521, train_loss_epoch=537]Epoch 122/400: 30%|███ | 121/400 [00:21<00:51, 5.37it/s, v_num=1, train_loss_step=521, train_loss_epoch=537]Epoch 122/400: 30%|███ | 122/400 [00:21<00:50, 5.55it/s, v_num=1, train_loss_step=521, train_loss_epoch=537]Epoch 122/400: 30%|███ | 122/400 [00:21<00:50, 5.55it/s, v_num=1, train_loss_step=531, train_loss_epoch=536]Epoch 123/400: 30%|███ | 122/400 [00:21<00:50, 5.55it/s, v_num=1, train_loss_step=531, train_loss_epoch=536]Epoch 123/400: 31%|███ | 123/400 [00:21<00:51, 5.39it/s, v_num=1, train_loss_step=531, train_loss_epoch=536]Epoch 123/400: 31%|███ | 123/400 [00:21<00:51, 5.39it/s, v_num=1, train_loss_step=553, train_loss_epoch=536]Epoch 124/400: 31%|███ | 123/400 [00:21<00:51, 5.39it/s, v_num=1, train_loss_step=553, train_loss_epoch=536]Epoch 124/400: 31%|███ | 124/400 [00:22<00:50, 5.50it/s, v_num=1, train_loss_step=553, train_loss_epoch=536]Epoch 124/400: 31%|███ | 124/400 [00:22<00:50, 5.50it/s, v_num=1, train_loss_step=548, train_loss_epoch=536]Epoch 125/400: 31%|███ | 124/400 [00:22<00:50, 5.50it/s, v_num=1, train_loss_step=548, train_loss_epoch=536]Epoch 125/400: 31%|███▏ | 125/400 [00:22<00:50, 5.44it/s, v_num=1, train_loss_step=548, train_loss_epoch=536]Epoch 125/400: 31%|███▏ | 125/400 [00:22<00:50, 5.44it/s, v_num=1, train_loss_step=544, train_loss_epoch=536]Epoch 126/400: 31%|███▏ | 125/400 [00:22<00:50, 5.44it/s, v_num=1, train_loss_step=544, train_loss_epoch=536]Epoch 126/400: 32%|███▏ | 126/400 [00:22<00:49, 5.50it/s, v_num=1, train_loss_step=544, train_loss_epoch=536]Epoch 126/400: 32%|███▏ | 126/400 [00:22<00:49, 5.50it/s, v_num=1, train_loss_step=550, train_loss_epoch=536]Epoch 127/400: 32%|███▏ | 126/400 [00:22<00:49, 5.50it/s, v_num=1, train_loss_step=550, train_loss_epoch=536]Epoch 127/400: 32%|███▏ | 127/400 [00:22<00:48, 5.65it/s, v_num=1, train_loss_step=550, train_loss_epoch=536]Epoch 127/400: 32%|███▏ | 127/400 [00:22<00:48, 5.65it/s, v_num=1, train_loss_step=552, train_loss_epoch=535]Epoch 128/400: 32%|███▏ | 127/400 [00:22<00:48, 5.65it/s, v_num=1, train_loss_step=552, train_loss_epoch=535]Epoch 128/400: 32%|███▏ | 128/400 [00:22<00:47, 5.73it/s, v_num=1, train_loss_step=552, train_loss_epoch=535]Epoch 128/400: 32%|███▏ | 128/400 [00:22<00:47, 5.73it/s, v_num=1, train_loss_step=548, train_loss_epoch=535]Epoch 129/400: 32%|███▏ | 128/400 [00:22<00:47, 5.73it/s, v_num=1, train_loss_step=548, train_loss_epoch=535]Epoch 129/400: 32%|███▏ | 129/400 [00:22<00:46, 5.85it/s, v_num=1, train_loss_step=548, train_loss_epoch=535]Epoch 129/400: 32%|███▏ | 129/400 [00:22<00:46, 5.85it/s, v_num=1, train_loss_step=521, train_loss_epoch=535]Epoch 130/400: 32%|███▏ | 129/400 [00:22<00:46, 5.85it/s, v_num=1, train_loss_step=521, train_loss_epoch=535]Epoch 130/400: 32%|███▎ | 130/400 [00:23<00:45, 5.95it/s, v_num=1, train_loss_step=521, train_loss_epoch=535]Epoch 130/400: 32%|███▎ | 130/400 [00:23<00:45, 5.95it/s, v_num=1, train_loss_step=507, train_loss_epoch=535]Epoch 131/400: 32%|███▎ | 130/400 [00:23<00:45, 5.95it/s, v_num=1, train_loss_step=507, train_loss_epoch=535]Epoch 131/400: 33%|███▎ | 131/400 [00:23<00:44, 6.02it/s, v_num=1, train_loss_step=507, train_loss_epoch=535]Epoch 131/400: 33%|███▎ | 131/400 [00:23<00:44, 6.02it/s, v_num=1, train_loss_step=568, train_loss_epoch=535]Epoch 132/400: 33%|███▎ | 131/400 [00:23<00:44, 6.02it/s, v_num=1, train_loss_step=568, train_loss_epoch=535]Epoch 132/400: 33%|███▎ | 132/400 [00:23<00:43, 6.10it/s, v_num=1, train_loss_step=568, train_loss_epoch=535]Epoch 132/400: 33%|███▎ | 132/400 [00:23<00:43, 6.10it/s, v_num=1, train_loss_step=552, train_loss_epoch=534]Epoch 133/400: 33%|███▎ | 132/400 [00:23<00:43, 6.10it/s, v_num=1, train_loss_step=552, train_loss_epoch=534]Epoch 133/400: 33%|███▎ | 133/400 [00:23<00:43, 6.10it/s, v_num=1, train_loss_step=552, train_loss_epoch=534]Epoch 133/400: 33%|███▎ | 133/400 [00:23<00:43, 6.10it/s, v_num=1, train_loss_step=566, train_loss_epoch=535]Epoch 134/400: 33%|███▎ | 133/400 [00:23<00:43, 6.10it/s, v_num=1, train_loss_step=566, train_loss_epoch=535]Epoch 134/400: 34%|███▎ | 134/400 [00:23<00:45, 5.79it/s, v_num=1, train_loss_step=566, train_loss_epoch=535]Epoch 134/400: 34%|███▎ | 134/400 [00:23<00:45, 5.79it/s, v_num=1, train_loss_step=544, train_loss_epoch=534]Epoch 135/400: 34%|███▎ | 134/400 [00:23<00:45, 5.79it/s, v_num=1, train_loss_step=544, train_loss_epoch=534]Epoch 135/400: 34%|███▍ | 135/400 [00:23<00:45, 5.83it/s, v_num=1, train_loss_step=544, train_loss_epoch=534]Epoch 135/400: 34%|███▍ | 135/400 [00:23<00:45, 5.83it/s, v_num=1, train_loss_step=500, train_loss_epoch=534]Epoch 136/400: 34%|███▍ | 135/400 [00:23<00:45, 5.83it/s, v_num=1, train_loss_step=500, train_loss_epoch=534]Epoch 136/400: 34%|███▍ | 136/400 [00:24<00:46, 5.63it/s, v_num=1, train_loss_step=500, train_loss_epoch=534]Epoch 136/400: 34%|███▍ | 136/400 [00:24<00:46, 5.63it/s, v_num=1, train_loss_step=510, train_loss_epoch=534]Epoch 137/400: 34%|███▍ | 136/400 [00:24<00:46, 5.63it/s, v_num=1, train_loss_step=510, train_loss_epoch=534]Epoch 137/400: 34%|███▍ | 137/400 [00:24<00:46, 5.67it/s, v_num=1, train_loss_step=510, train_loss_epoch=534]Epoch 137/400: 34%|███▍ | 137/400 [00:24<00:46, 5.67it/s, v_num=1, train_loss_step=542, train_loss_epoch=533]Epoch 138/400: 34%|███▍ | 137/400 [00:24<00:46, 5.67it/s, v_num=1, train_loss_step=542, train_loss_epoch=533]Epoch 138/400: 34%|███▍ | 138/400 [00:24<00:47, 5.48it/s, v_num=1, train_loss_step=542, train_loss_epoch=533]Epoch 138/400: 34%|███▍ | 138/400 [00:24<00:47, 5.48it/s, v_num=1, train_loss_step=542, train_loss_epoch=532]Epoch 139/400: 34%|███▍ | 138/400 [00:24<00:47, 5.48it/s, v_num=1, train_loss_step=542, train_loss_epoch=532]Epoch 139/400: 35%|███▍ | 139/400 [00:24<00:47, 5.44it/s, v_num=1, train_loss_step=542, train_loss_epoch=532]Epoch 139/400: 35%|███▍ | 139/400 [00:24<00:47, 5.44it/s, v_num=1, train_loss_step=542, train_loss_epoch=533]Epoch 140/400: 35%|███▍ | 139/400 [00:24<00:47, 5.44it/s, v_num=1, train_loss_step=542, train_loss_epoch=533]Epoch 140/400: 35%|███▌ | 140/400 [00:24<00:48, 5.38it/s, v_num=1, train_loss_step=542, train_loss_epoch=533]Epoch 140/400: 35%|███▌ | 140/400 [00:24<00:48, 5.38it/s, v_num=1, train_loss_step=534, train_loss_epoch=532]Epoch 141/400: 35%|███▌ | 140/400 [00:24<00:48, 5.38it/s, v_num=1, train_loss_step=534, train_loss_epoch=532]Epoch 141/400: 35%|███▌ | 141/400 [00:25<00:49, 5.28it/s, v_num=1, train_loss_step=534, train_loss_epoch=532]Epoch 141/400: 35%|███▌ | 141/400 [00:25<00:49, 5.28it/s, v_num=1, train_loss_step=523, train_loss_epoch=532]Epoch 142/400: 35%|███▌ | 141/400 [00:25<00:49, 5.28it/s, v_num=1, train_loss_step=523, train_loss_epoch=532]Epoch 142/400: 36%|███▌ | 142/400 [00:25<00:48, 5.29it/s, v_num=1, train_loss_step=523, train_loss_epoch=532]Epoch 142/400: 36%|███▌ | 142/400 [00:25<00:48, 5.29it/s, v_num=1, train_loss_step=503, train_loss_epoch=532]Epoch 143/400: 36%|███▌ | 142/400 [00:25<00:48, 5.29it/s, v_num=1, train_loss_step=503, train_loss_epoch=532]Epoch 143/400: 36%|███▌ | 143/400 [00:25<00:47, 5.41it/s, v_num=1, train_loss_step=503, train_loss_epoch=532]Epoch 143/400: 36%|███▌ | 143/400 [00:25<00:47, 5.41it/s, v_num=1, train_loss_step=555, train_loss_epoch=532]Epoch 144/400: 36%|███▌ | 143/400 [00:25<00:47, 5.41it/s, v_num=1, train_loss_step=555, train_loss_epoch=532]Epoch 144/400: 36%|███▌ | 144/400 [00:25<00:46, 5.56it/s, v_num=1, train_loss_step=555, train_loss_epoch=532]Epoch 144/400: 36%|███▌ | 144/400 [00:25<00:46, 5.56it/s, v_num=1, train_loss_step=536, train_loss_epoch=532]Epoch 145/400: 36%|███▌ | 144/400 [00:25<00:46, 5.56it/s, v_num=1, train_loss_step=536, train_loss_epoch=532]Epoch 145/400: 36%|███▋ | 145/400 [00:25<00:45, 5.60it/s, v_num=1, train_loss_step=536, train_loss_epoch=532]Epoch 145/400: 36%|███▋ | 145/400 [00:25<00:45, 5.60it/s, v_num=1, train_loss_step=514, train_loss_epoch=532]Epoch 146/400: 36%|███▋ | 145/400 [00:25<00:45, 5.60it/s, v_num=1, train_loss_step=514, train_loss_epoch=532]Epoch 146/400: 36%|███▋ | 146/400 [00:25<00:45, 5.64it/s, v_num=1, train_loss_step=514, train_loss_epoch=532]Epoch 146/400: 36%|███▋ | 146/400 [00:25<00:45, 5.64it/s, v_num=1, train_loss_step=551, train_loss_epoch=531]Epoch 147/400: 36%|███▋ | 146/400 [00:25<00:45, 5.64it/s, v_num=1, train_loss_step=551, train_loss_epoch=531]Epoch 147/400: 37%|███▋ | 147/400 [00:26<00:46, 5.40it/s, v_num=1, train_loss_step=551, train_loss_epoch=531]Epoch 147/400: 37%|███▋ | 147/400 [00:26<00:46, 5.40it/s, v_num=1, train_loss_step=500, train_loss_epoch=531]Epoch 148/400: 37%|███▋ | 147/400 [00:26<00:46, 5.40it/s, v_num=1, train_loss_step=500, train_loss_epoch=531]Epoch 148/400: 37%|███▋ | 148/400 [00:26<00:45, 5.51it/s, v_num=1, train_loss_step=500, train_loss_epoch=531]Epoch 148/400: 37%|███▋ | 148/400 [00:26<00:45, 5.51it/s, v_num=1, train_loss_step=566, train_loss_epoch=531]Epoch 149/400: 37%|███▋ | 148/400 [00:26<00:45, 5.51it/s, v_num=1, train_loss_step=566, train_loss_epoch=531]Epoch 149/400: 37%|███▋ | 149/400 [00:26<00:43, 5.74it/s, v_num=1, train_loss_step=566, train_loss_epoch=531]Epoch 149/400: 37%|███▋ | 149/400 [00:26<00:43, 5.74it/s, v_num=1, train_loss_step=538, train_loss_epoch=531]Epoch 150/400: 37%|███▋ | 149/400 [00:26<00:43, 5.74it/s, v_num=1, train_loss_step=538, train_loss_epoch=531]Epoch 150/400: 38%|███▊ | 150/400 [00:26<00:43, 5.79it/s, v_num=1, train_loss_step=538, train_loss_epoch=531]Epoch 150/400: 38%|███▊ | 150/400 [00:26<00:43, 5.79it/s, v_num=1, train_loss_step=546, train_loss_epoch=530]Epoch 151/400: 38%|███▊ | 150/400 [00:26<00:43, 5.79it/s, v_num=1, train_loss_step=546, train_loss_epoch=530]Epoch 151/400: 38%|███▊ | 151/400 [00:26<00:43, 5.76it/s, v_num=1, train_loss_step=546, train_loss_epoch=530]Epoch 151/400: 38%|███▊ | 151/400 [00:26<00:43, 5.76it/s, v_num=1, train_loss_step=533, train_loss_epoch=530]Epoch 152/400: 38%|███▊ | 151/400 [00:26<00:43, 5.76it/s, v_num=1, train_loss_step=533, train_loss_epoch=530]Epoch 152/400: 38%|███▊ | 152/400 [00:26<00:41, 5.93it/s, v_num=1, train_loss_step=533, train_loss_epoch=530]Epoch 152/400: 38%|███▊ | 152/400 [00:26<00:41, 5.93it/s, v_num=1, train_loss_step=515, train_loss_epoch=530]Epoch 153/400: 38%|███▊ | 152/400 [00:26<00:41, 5.93it/s, v_num=1, train_loss_step=515, train_loss_epoch=530]Epoch 153/400: 38%|███▊ | 153/400 [00:27<00:40, 6.07it/s, v_num=1, train_loss_step=515, train_loss_epoch=530]Epoch 153/400: 38%|███▊ | 153/400 [00:27<00:40, 6.07it/s, v_num=1, train_loss_step=522, train_loss_epoch=530]Epoch 154/400: 38%|███▊ | 153/400 [00:27<00:40, 6.07it/s, v_num=1, train_loss_step=522, train_loss_epoch=530]Epoch 154/400: 38%|███▊ | 154/400 [00:27<00:40, 6.10it/s, v_num=1, train_loss_step=522, train_loss_epoch=530]Epoch 154/400: 38%|███▊ | 154/400 [00:27<00:40, 6.10it/s, v_num=1, train_loss_step=510, train_loss_epoch=529]Epoch 155/400: 38%|███▊ | 154/400 [00:27<00:40, 6.10it/s, v_num=1, train_loss_step=510, train_loss_epoch=529]Epoch 155/400: 39%|███▉ | 155/400 [00:27<00:40, 6.09it/s, v_num=1, train_loss_step=510, train_loss_epoch=529]Epoch 155/400: 39%|███▉ | 155/400 [00:27<00:40, 6.09it/s, v_num=1, train_loss_step=535, train_loss_epoch=529]Epoch 156/400: 39%|███▉ | 155/400 [00:27<00:40, 6.09it/s, v_num=1, train_loss_step=535, train_loss_epoch=529]Epoch 156/400: 39%|███▉ | 156/400 [00:27<00:40, 5.99it/s, v_num=1, train_loss_step=535, train_loss_epoch=529]Epoch 156/400: 39%|███▉ | 156/400 [00:27<00:40, 5.99it/s, v_num=1, train_loss_step=516, train_loss_epoch=529]Epoch 157/400: 39%|███▉ | 156/400 [00:27<00:40, 5.99it/s, v_num=1, train_loss_step=516, train_loss_epoch=529]Epoch 157/400: 39%|███▉ | 157/400 [00:27<00:42, 5.73it/s, v_num=1, train_loss_step=516, train_loss_epoch=529]Epoch 157/400: 39%|███▉ | 157/400 [00:27<00:42, 5.73it/s, v_num=1, train_loss_step=558, train_loss_epoch=529]Epoch 158/400: 39%|███▉ | 157/400 [00:27<00:42, 5.73it/s, v_num=1, train_loss_step=558, train_loss_epoch=529]Epoch 158/400: 40%|███▉ | 158/400 [00:27<00:41, 5.83it/s, v_num=1, train_loss_step=558, train_loss_epoch=529]Epoch 158/400: 40%|███▉ | 158/400 [00:27<00:41, 5.83it/s, v_num=1, train_loss_step=509, train_loss_epoch=529]Epoch 159/400: 40%|███▉ | 158/400 [00:27<00:41, 5.83it/s, v_num=1, train_loss_step=509, train_loss_epoch=529]Epoch 159/400: 40%|███▉ | 159/400 [00:28<00:40, 5.91it/s, v_num=1, train_loss_step=509, train_loss_epoch=529]Epoch 159/400: 40%|███▉ | 159/400 [00:28<00:40, 5.91it/s, v_num=1, train_loss_step=502, train_loss_epoch=528]Epoch 160/400: 40%|███▉ | 159/400 [00:28<00:40, 5.91it/s, v_num=1, train_loss_step=502, train_loss_epoch=528]Epoch 160/400: 40%|████ | 160/400 [00:28<00:40, 5.98it/s, v_num=1, train_loss_step=502, train_loss_epoch=528]Epoch 160/400: 40%|████ | 160/400 [00:28<00:40, 5.98it/s, v_num=1, train_loss_step=518, train_loss_epoch=529]Epoch 161/400: 40%|████ | 160/400 [00:28<00:40, 5.98it/s, v_num=1, train_loss_step=518, train_loss_epoch=529]Epoch 161/400: 40%|████ | 161/400 [00:28<00:41, 5.75it/s, v_num=1, train_loss_step=518, train_loss_epoch=529]Epoch 161/400: 40%|████ | 161/400 [00:28<00:41, 5.75it/s, v_num=1, train_loss_step=522, train_loss_epoch=528]Epoch 162/400: 40%|████ | 161/400 [00:28<00:41, 5.75it/s, v_num=1, train_loss_step=522, train_loss_epoch=528]Epoch 162/400: 40%|████ | 162/400 [00:28<00:41, 5.68it/s, v_num=1, train_loss_step=522, train_loss_epoch=528]Epoch 162/400: 40%|████ | 162/400 [00:28<00:41, 5.68it/s, v_num=1, train_loss_step=528, train_loss_epoch=528]Epoch 163/400: 40%|████ | 162/400 [00:28<00:41, 5.68it/s, v_num=1, train_loss_step=528, train_loss_epoch=528]Epoch 163/400: 41%|████ | 163/400 [00:28<00:41, 5.73it/s, v_num=1, train_loss_step=528, train_loss_epoch=528]Epoch 163/400: 41%|████ | 163/400 [00:28<00:41, 5.73it/s, v_num=1, train_loss_step=544, train_loss_epoch=528]Epoch 164/400: 41%|████ | 163/400 [00:28<00:41, 5.73it/s, v_num=1, train_loss_step=544, train_loss_epoch=528]Epoch 164/400: 41%|████ | 164/400 [00:29<00:40, 5.76it/s, v_num=1, train_loss_step=544, train_loss_epoch=528]Epoch 164/400: 41%|████ | 164/400 [00:29<00:40, 5.76it/s, v_num=1, train_loss_step=503, train_loss_epoch=528]Epoch 165/400: 41%|████ | 164/400 [00:29<00:40, 5.76it/s, v_num=1, train_loss_step=503, train_loss_epoch=528]Epoch 165/400: 41%|████▏ | 165/400 [00:29<00:40, 5.79it/s, v_num=1, train_loss_step=503, train_loss_epoch=528]Epoch 165/400: 41%|████▏ | 165/400 [00:29<00:40, 5.79it/s, v_num=1, train_loss_step=537, train_loss_epoch=528]Epoch 166/400: 41%|████▏ | 165/400 [00:29<00:40, 5.79it/s, v_num=1, train_loss_step=537, train_loss_epoch=528]Epoch 166/400: 42%|████▏ | 166/400 [00:29<00:39, 5.86it/s, v_num=1, train_loss_step=537, train_loss_epoch=528]Epoch 166/400: 42%|████▏ | 166/400 [00:29<00:39, 5.86it/s, v_num=1, train_loss_step=518, train_loss_epoch=527]Epoch 167/400: 42%|████▏ | 166/400 [00:29<00:39, 5.86it/s, v_num=1, train_loss_step=518, train_loss_epoch=527]Epoch 167/400: 42%|████▏ | 167/400 [00:29<00:39, 5.91it/s, v_num=1, train_loss_step=518, train_loss_epoch=527]Epoch 167/400: 42%|████▏ | 167/400 [00:29<00:39, 5.91it/s, v_num=1, train_loss_step=565, train_loss_epoch=527]Epoch 168/400: 42%|████▏ | 167/400 [00:29<00:39, 5.91it/s, v_num=1, train_loss_step=565, train_loss_epoch=527]Epoch 168/400: 42%|████▏ | 168/400 [00:29<00:40, 5.72it/s, v_num=1, train_loss_step=565, train_loss_epoch=527]Epoch 168/400: 42%|████▏ | 168/400 [00:29<00:40, 5.72it/s, v_num=1, train_loss_step=527, train_loss_epoch=527]Epoch 169/400: 42%|████▏ | 168/400 [00:29<00:40, 5.72it/s, v_num=1, train_loss_step=527, train_loss_epoch=527]Epoch 169/400: 42%|████▏ | 169/400 [00:29<00:39, 5.82it/s, v_num=1, train_loss_step=527, train_loss_epoch=527]Epoch 169/400: 42%|████▏ | 169/400 [00:29<00:39, 5.82it/s, v_num=1, train_loss_step=528, train_loss_epoch=527]Epoch 170/400: 42%|████▏ | 169/400 [00:29<00:39, 5.82it/s, v_num=1, train_loss_step=528, train_loss_epoch=527]Epoch 170/400: 42%|████▎ | 170/400 [00:30<00:40, 5.73it/s, v_num=1, train_loss_step=528, train_loss_epoch=527]Epoch 170/400: 42%|████▎ | 170/400 [00:30<00:40, 5.73it/s, v_num=1, train_loss_step=525, train_loss_epoch=527]Epoch 171/400: 42%|████▎ | 170/400 [00:30<00:40, 5.73it/s, v_num=1, train_loss_step=525, train_loss_epoch=527]Epoch 171/400: 43%|████▎ | 171/400 [00:30<00:39, 5.82it/s, v_num=1, train_loss_step=525, train_loss_epoch=527]Epoch 171/400: 43%|████▎ | 171/400 [00:30<00:39, 5.82it/s, v_num=1, train_loss_step=548, train_loss_epoch=526]Epoch 172/400: 43%|████▎ | 171/400 [00:30<00:39, 5.82it/s, v_num=1, train_loss_step=548, train_loss_epoch=526]Epoch 172/400: 43%|████▎ | 172/400 [00:30<00:39, 5.73it/s, v_num=1, train_loss_step=548, train_loss_epoch=526]Epoch 172/400: 43%|████▎ | 172/400 [00:30<00:39, 5.73it/s, v_num=1, train_loss_step=524, train_loss_epoch=526]Epoch 173/400: 43%|████▎ | 172/400 [00:30<00:39, 5.73it/s, v_num=1, train_loss_step=524, train_loss_epoch=526]Epoch 173/400: 43%|████▎ | 173/400 [00:30<00:40, 5.58it/s, v_num=1, train_loss_step=524, train_loss_epoch=526]Epoch 173/400: 43%|████▎ | 173/400 [00:30<00:40, 5.58it/s, v_num=1, train_loss_step=548, train_loss_epoch=526]Epoch 174/400: 43%|████▎ | 173/400 [00:30<00:40, 5.58it/s, v_num=1, train_loss_step=548, train_loss_epoch=526]Epoch 174/400: 44%|████▎ | 174/400 [00:30<00:41, 5.43it/s, v_num=1, train_loss_step=548, train_loss_epoch=526]Epoch 174/400: 44%|████▎ | 174/400 [00:30<00:41, 5.43it/s, v_num=1, train_loss_step=531, train_loss_epoch=526]Epoch 175/400: 44%|████▎ | 174/400 [00:30<00:41, 5.43it/s, v_num=1, train_loss_step=531, train_loss_epoch=526]Epoch 175/400: 44%|████▍ | 175/400 [00:30<00:40, 5.52it/s, v_num=1, train_loss_step=531, train_loss_epoch=526]Epoch 175/400: 44%|████▍ | 175/400 [00:30<00:40, 5.52it/s, v_num=1, train_loss_step=523, train_loss_epoch=526]Epoch 176/400: 44%|████▍ | 175/400 [00:30<00:40, 5.52it/s, v_num=1, train_loss_step=523, train_loss_epoch=526]Epoch 176/400: 44%|████▍ | 176/400 [00:31<00:39, 5.74it/s, v_num=1, train_loss_step=523, train_loss_epoch=526]Epoch 176/400: 44%|████▍ | 176/400 [00:31<00:39, 5.74it/s, v_num=1, train_loss_step=514, train_loss_epoch=526]Epoch 177/400: 44%|████▍ | 176/400 [00:31<00:39, 5.74it/s, v_num=1, train_loss_step=514, train_loss_epoch=526]Epoch 177/400: 44%|████▍ | 177/400 [00:31<00:39, 5.67it/s, v_num=1, train_loss_step=514, train_loss_epoch=526]Epoch 177/400: 44%|████▍ | 177/400 [00:31<00:39, 5.67it/s, v_num=1, train_loss_step=537, train_loss_epoch=525]Epoch 178/400: 44%|████▍ | 177/400 [00:31<00:39, 5.67it/s, v_num=1, train_loss_step=537, train_loss_epoch=525]Epoch 178/400: 44%|████▍ | 178/400 [00:31<00:39, 5.65it/s, v_num=1, train_loss_step=537, train_loss_epoch=525]Epoch 178/400: 44%|████▍ | 178/400 [00:31<00:39, 5.65it/s, v_num=1, train_loss_step=516, train_loss_epoch=526]Epoch 179/400: 44%|████▍ | 178/400 [00:31<00:39, 5.65it/s, v_num=1, train_loss_step=516, train_loss_epoch=526]Epoch 179/400: 45%|████▍ | 179/400 [00:31<00:40, 5.51it/s, v_num=1, train_loss_step=516, train_loss_epoch=526]Epoch 179/400: 45%|████▍ | 179/400 [00:31<00:40, 5.51it/s, v_num=1, train_loss_step=495, train_loss_epoch=525]Epoch 180/400: 45%|████▍ | 179/400 [00:31<00:40, 5.51it/s, v_num=1, train_loss_step=495, train_loss_epoch=525]Epoch 180/400: 45%|████▌ | 180/400 [00:31<00:40, 5.43it/s, v_num=1, train_loss_step=495, train_loss_epoch=525]Epoch 180/400: 45%|████▌ | 180/400 [00:31<00:40, 5.43it/s, v_num=1, train_loss_step=540, train_loss_epoch=525]Epoch 181/400: 45%|████▌ | 180/400 [00:31<00:40, 5.43it/s, v_num=1, train_loss_step=540, train_loss_epoch=525]Epoch 181/400: 45%|████▌ | 181/400 [00:32<00:40, 5.38it/s, v_num=1, train_loss_step=540, train_loss_epoch=525]Epoch 181/400: 45%|████▌ | 181/400 [00:32<00:40, 5.38it/s, v_num=1, train_loss_step=577, train_loss_epoch=525]Epoch 182/400: 45%|████▌ | 181/400 [00:32<00:40, 5.38it/s, v_num=1, train_loss_step=577, train_loss_epoch=525]Epoch 182/400: 46%|████▌ | 182/400 [00:32<00:39, 5.54it/s, v_num=1, train_loss_step=577, train_loss_epoch=525]Epoch 182/400: 46%|████▌ | 182/400 [00:32<00:39, 5.54it/s, v_num=1, train_loss_step=506, train_loss_epoch=525]Epoch 183/400: 46%|████▌ | 182/400 [00:32<00:39, 5.54it/s, v_num=1, train_loss_step=506, train_loss_epoch=525]Epoch 183/400: 46%|████▌ | 183/400 [00:32<00:38, 5.67it/s, v_num=1, train_loss_step=506, train_loss_epoch=525]Epoch 183/400: 46%|████▌ | 183/400 [00:32<00:38, 5.67it/s, v_num=1, train_loss_step=542, train_loss_epoch=524]Epoch 184/400: 46%|████▌ | 183/400 [00:32<00:38, 5.67it/s, v_num=1, train_loss_step=542, train_loss_epoch=524]Epoch 184/400: 46%|████▌ | 184/400 [00:32<00:39, 5.45it/s, v_num=1, train_loss_step=542, train_loss_epoch=524]Epoch 184/400: 46%|████▌ | 184/400 [00:32<00:39, 5.45it/s, v_num=1, train_loss_step=543, train_loss_epoch=524]Epoch 185/400: 46%|████▌ | 184/400 [00:32<00:39, 5.45it/s, v_num=1, train_loss_step=543, train_loss_epoch=524]Epoch 185/400: 46%|████▋ | 185/400 [00:32<00:40, 5.35it/s, v_num=1, train_loss_step=543, train_loss_epoch=524]Epoch 185/400: 46%|████▋ | 185/400 [00:32<00:40, 5.35it/s, v_num=1, train_loss_step=537, train_loss_epoch=524]Epoch 186/400: 46%|████▋ | 185/400 [00:32<00:40, 5.35it/s, v_num=1, train_loss_step=537, train_loss_epoch=524]Epoch 186/400: 46%|████▋ | 186/400 [00:32<00:40, 5.27it/s, v_num=1, train_loss_step=537, train_loss_epoch=524]Epoch 186/400: 46%|████▋ | 186/400 [00:32<00:40, 5.27it/s, v_num=1, train_loss_step=503, train_loss_epoch=524]Epoch 187/400: 46%|████▋ | 186/400 [00:32<00:40, 5.27it/s, v_num=1, train_loss_step=503, train_loss_epoch=524]Epoch 187/400: 47%|████▋ | 187/400 [00:33<00:41, 5.17it/s, v_num=1, train_loss_step=503, train_loss_epoch=524]Epoch 187/400: 47%|████▋ | 187/400 [00:33<00:41, 5.17it/s, v_num=1, train_loss_step=506, train_loss_epoch=524]Epoch 188/400: 47%|████▋ | 187/400 [00:33<00:41, 5.17it/s, v_num=1, train_loss_step=506, train_loss_epoch=524]Epoch 188/400: 47%|████▋ | 188/400 [00:33<00:39, 5.33it/s, v_num=1, train_loss_step=506, train_loss_epoch=524]Epoch 188/400: 47%|████▋ | 188/400 [00:33<00:39, 5.33it/s, v_num=1, train_loss_step=540, train_loss_epoch=524]Epoch 189/400: 47%|████▋ | 188/400 [00:33<00:39, 5.33it/s, v_num=1, train_loss_step=540, train_loss_epoch=524]Epoch 189/400: 47%|████▋ | 189/400 [00:33<00:37, 5.58it/s, v_num=1, train_loss_step=540, train_loss_epoch=524]Epoch 189/400: 47%|████▋ | 189/400 [00:33<00:37, 5.58it/s, v_num=1, train_loss_step=485, train_loss_epoch=523]Epoch 190/400: 47%|████▋ | 189/400 [00:33<00:37, 5.58it/s, v_num=1, train_loss_step=485, train_loss_epoch=523]Epoch 190/400: 48%|████▊ | 190/400 [00:33<00:37, 5.58it/s, v_num=1, train_loss_step=485, train_loss_epoch=523]Epoch 190/400: 48%|████▊ | 190/400 [00:33<00:37, 5.58it/s, v_num=1, train_loss_step=541, train_loss_epoch=523]Epoch 191/400: 48%|████▊ | 190/400 [00:33<00:37, 5.58it/s, v_num=1, train_loss_step=541, train_loss_epoch=523]Epoch 191/400: 48%|████▊ | 191/400 [00:33<00:37, 5.52it/s, v_num=1, train_loss_step=541, train_loss_epoch=523]Epoch 191/400: 48%|████▊ | 191/400 [00:33<00:37, 5.52it/s, v_num=1, train_loss_step=529, train_loss_epoch=524]Epoch 192/400: 48%|████▊ | 191/400 [00:33<00:37, 5.52it/s, v_num=1, train_loss_step=529, train_loss_epoch=524]Epoch 192/400: 48%|████▊ | 192/400 [00:34<00:38, 5.41it/s, v_num=1, train_loss_step=529, train_loss_epoch=524]Epoch 192/400: 48%|████▊ | 192/400 [00:34<00:38, 5.41it/s, v_num=1, train_loss_step=517, train_loss_epoch=523]Epoch 193/400: 48%|████▊ | 192/400 [00:34<00:38, 5.41it/s, v_num=1, train_loss_step=517, train_loss_epoch=523]Epoch 193/400: 48%|████▊ | 193/400 [00:34<00:38, 5.34it/s, v_num=1, train_loss_step=517, train_loss_epoch=523]Epoch 193/400: 48%|████▊ | 193/400 [00:34<00:38, 5.34it/s, v_num=1, train_loss_step=501, train_loss_epoch=523]Epoch 194/400: 48%|████▊ | 193/400 [00:34<00:38, 5.34it/s, v_num=1, train_loss_step=501, train_loss_epoch=523]Epoch 194/400: 48%|████▊ | 194/400 [00:34<00:37, 5.49it/s, v_num=1, train_loss_step=501, train_loss_epoch=523]Epoch 194/400: 48%|████▊ | 194/400 [00:34<00:37, 5.49it/s, v_num=1, train_loss_step=529, train_loss_epoch=523]Epoch 195/400: 48%|████▊ | 194/400 [00:34<00:37, 5.49it/s, v_num=1, train_loss_step=529, train_loss_epoch=523]Epoch 195/400: 49%|████▉ | 195/400 [00:34<00:36, 5.57it/s, v_num=1, train_loss_step=529, train_loss_epoch=523]Epoch 195/400: 49%|████▉ | 195/400 [00:34<00:36, 5.57it/s, v_num=1, train_loss_step=525, train_loss_epoch=522]Epoch 196/400: 49%|████▉ | 195/400 [00:34<00:36, 5.57it/s, v_num=1, train_loss_step=525, train_loss_epoch=522]Epoch 196/400: 49%|████▉ | 196/400 [00:34<00:35, 5.75it/s, v_num=1, train_loss_step=525, train_loss_epoch=522]Epoch 196/400: 49%|████▉ | 196/400 [00:34<00:35, 5.75it/s, v_num=1, train_loss_step=493, train_loss_epoch=522]Epoch 197/400: 49%|████▉ | 196/400 [00:34<00:35, 5.75it/s, v_num=1, train_loss_step=493, train_loss_epoch=522]Epoch 197/400: 49%|████▉ | 197/400 [00:34<00:35, 5.64it/s, v_num=1, train_loss_step=493, train_loss_epoch=522]Epoch 197/400: 49%|████▉ | 197/400 [00:34<00:35, 5.64it/s, v_num=1, train_loss_step=540, train_loss_epoch=523]Epoch 198/400: 49%|████▉ | 197/400 [00:34<00:35, 5.64it/s, v_num=1, train_loss_step=540, train_loss_epoch=523]Epoch 198/400: 50%|████▉ | 198/400 [00:35<00:36, 5.47it/s, v_num=1, train_loss_step=540, train_loss_epoch=523]Epoch 198/400: 50%|████▉ | 198/400 [00:35<00:36, 5.47it/s, v_num=1, train_loss_step=510, train_loss_epoch=522]Epoch 199/400: 50%|████▉ | 198/400 [00:35<00:36, 5.47it/s, v_num=1, train_loss_step=510, train_loss_epoch=522]Epoch 199/400: 50%|████▉ | 199/400 [00:35<00:37, 5.34it/s, v_num=1, train_loss_step=510, train_loss_epoch=522]Epoch 199/400: 50%|████▉ | 199/400 [00:35<00:37, 5.34it/s, v_num=1, train_loss_step=499, train_loss_epoch=522]Epoch 200/400: 50%|████▉ | 199/400 [00:35<00:37, 5.34it/s, v_num=1, train_loss_step=499, train_loss_epoch=522]Epoch 200/400: 50%|█████ | 200/400 [00:35<00:38, 5.22it/s, v_num=1, train_loss_step=499, train_loss_epoch=522]Epoch 200/400: 50%|█████ | 200/400 [00:35<00:38, 5.22it/s, v_num=1, train_loss_step=520, train_loss_epoch=522]Epoch 201/400: 50%|█████ | 200/400 [00:35<00:38, 5.22it/s, v_num=1, train_loss_step=520, train_loss_epoch=522]Epoch 201/400: 50%|█████ | 201/400 [00:35<00:37, 5.30it/s, v_num=1, train_loss_step=520, train_loss_epoch=522]Epoch 201/400: 50%|█████ | 201/400 [00:35<00:37, 5.30it/s, v_num=1, train_loss_step=514, train_loss_epoch=522]Epoch 202/400: 50%|█████ | 201/400 [00:35<00:37, 5.30it/s, v_num=1, train_loss_step=514, train_loss_epoch=522]Epoch 202/400: 50%|█████ | 202/400 [00:35<00:35, 5.52it/s, v_num=1, train_loss_step=514, train_loss_epoch=522]Epoch 202/400: 50%|█████ | 202/400 [00:35<00:35, 5.52it/s, v_num=1, train_loss_step=525, train_loss_epoch=522]Epoch 203/400: 50%|█████ | 202/400 [00:35<00:35, 5.52it/s, v_num=1, train_loss_step=525, train_loss_epoch=522]Epoch 203/400: 51%|█████ | 203/400 [00:36<00:34, 5.66it/s, v_num=1, train_loss_step=525, train_loss_epoch=522]Epoch 203/400: 51%|█████ | 203/400 [00:36<00:34, 5.66it/s, v_num=1, train_loss_step=510, train_loss_epoch=522]Epoch 204/400: 51%|█████ | 203/400 [00:36<00:34, 5.66it/s, v_num=1, train_loss_step=510, train_loss_epoch=522]Epoch 204/400: 51%|█████ | 204/400 [00:36<00:35, 5.50it/s, v_num=1, train_loss_step=510, train_loss_epoch=522]Epoch 204/400: 51%|█████ | 204/400 [00:36<00:35, 5.50it/s, v_num=1, train_loss_step=507, train_loss_epoch=521]Epoch 205/400: 51%|█████ | 204/400 [00:36<00:35, 5.50it/s, v_num=1, train_loss_step=507, train_loss_epoch=521]Epoch 205/400: 51%|█████▏ | 205/400 [00:36<00:35, 5.53it/s, v_num=1, train_loss_step=507, train_loss_epoch=521]Epoch 205/400: 51%|█████▏ | 205/400 [00:36<00:35, 5.53it/s, v_num=1, train_loss_step=546, train_loss_epoch=521]Epoch 206/400: 51%|█████▏ | 205/400 [00:36<00:35, 5.53it/s, v_num=1, train_loss_step=546, train_loss_epoch=521]Epoch 206/400: 52%|█████▏ | 206/400 [00:36<00:35, 5.50it/s, v_num=1, train_loss_step=546, train_loss_epoch=521]Epoch 206/400: 52%|█████▏ | 206/400 [00:36<00:35, 5.50it/s, v_num=1, train_loss_step=553, train_loss_epoch=521]Epoch 207/400: 52%|█████▏ | 206/400 [00:36<00:35, 5.50it/s, v_num=1, train_loss_step=553, train_loss_epoch=521]Epoch 207/400: 52%|█████▏ | 207/400 [00:36<00:35, 5.38it/s, v_num=1, train_loss_step=553, train_loss_epoch=521]Epoch 207/400: 52%|█████▏ | 207/400 [00:36<00:35, 5.38it/s, v_num=1, train_loss_step=533, train_loss_epoch=521]Epoch 208/400: 52%|█████▏ | 207/400 [00:36<00:35, 5.38it/s, v_num=1, train_loss_step=533, train_loss_epoch=521]Epoch 208/400: 52%|█████▏ | 208/400 [00:37<00:35, 5.46it/s, v_num=1, train_loss_step=533, train_loss_epoch=521]Epoch 208/400: 52%|█████▏ | 208/400 [00:37<00:35, 5.46it/s, v_num=1, train_loss_step=538, train_loss_epoch=521]Epoch 209/400: 52%|█████▏ | 208/400 [00:37<00:35, 5.46it/s, v_num=1, train_loss_step=538, train_loss_epoch=521]Epoch 209/400: 52%|█████▏ | 209/400 [00:37<00:33, 5.67it/s, v_num=1, train_loss_step=538, train_loss_epoch=521]Epoch 209/400: 52%|█████▏ | 209/400 [00:37<00:33, 5.67it/s, v_num=1, train_loss_step=522, train_loss_epoch=521]Epoch 210/400: 52%|█████▏ | 209/400 [00:37<00:33, 5.67it/s, v_num=1, train_loss_step=522, train_loss_epoch=521]Epoch 210/400: 52%|█████▎ | 210/400 [00:37<00:32, 5.79it/s, v_num=1, train_loss_step=522, train_loss_epoch=521]Epoch 210/400: 52%|█████▎ | 210/400 [00:37<00:32, 5.79it/s, v_num=1, train_loss_step=522, train_loss_epoch=520]Epoch 211/400: 52%|█████▎ | 210/400 [00:37<00:32, 5.79it/s, v_num=1, train_loss_step=522, train_loss_epoch=520]Epoch 211/400: 53%|█████▎ | 211/400 [00:37<00:33, 5.67it/s, v_num=1, train_loss_step=522, train_loss_epoch=520]Epoch 211/400: 53%|█████▎ | 211/400 [00:37<00:33, 5.67it/s, v_num=1, train_loss_step=554, train_loss_epoch=520]Epoch 212/400: 53%|█████▎ | 211/400 [00:37<00:33, 5.67it/s, v_num=1, train_loss_step=554, train_loss_epoch=520]Epoch 212/400: 53%|█████▎ | 212/400 [00:37<00:33, 5.69it/s, v_num=1, train_loss_step=554, train_loss_epoch=520]Epoch 212/400: 53%|█████▎ | 212/400 [00:37<00:33, 5.69it/s, v_num=1, train_loss_step=524, train_loss_epoch=521]Epoch 213/400: 53%|█████▎ | 212/400 [00:37<00:33, 5.69it/s, v_num=1, train_loss_step=524, train_loss_epoch=521]Epoch 213/400: 53%|█████▎ | 213/400 [00:37<00:33, 5.66it/s, v_num=1, train_loss_step=524, train_loss_epoch=521]Epoch 213/400: 53%|█████▎ | 213/400 [00:37<00:33, 5.66it/s, v_num=1, train_loss_step=526, train_loss_epoch=521]Epoch 214/400: 53%|█████▎ | 213/400 [00:37<00:33, 5.66it/s, v_num=1, train_loss_step=526, train_loss_epoch=521]Epoch 214/400: 54%|█████▎ | 214/400 [00:38<00:33, 5.55it/s, v_num=1, train_loss_step=526, train_loss_epoch=521]Epoch 214/400: 54%|█████▎ | 214/400 [00:38<00:33, 5.55it/s, v_num=1, train_loss_step=515, train_loss_epoch=521]Epoch 215/400: 54%|█████▎ | 214/400 [00:38<00:33, 5.55it/s, v_num=1, train_loss_step=515, train_loss_epoch=521]Epoch 215/400: 54%|█████▍ | 215/400 [00:38<00:34, 5.42it/s, v_num=1, train_loss_step=515, train_loss_epoch=521]Epoch 215/400: 54%|█████▍ | 215/400 [00:38<00:34, 5.42it/s, v_num=1, train_loss_step=549, train_loss_epoch=520]Epoch 216/400: 54%|█████▍ | 215/400 [00:38<00:34, 5.42it/s, v_num=1, train_loss_step=549, train_loss_epoch=520]Epoch 216/400: 54%|█████▍ | 216/400 [00:38<00:32, 5.60it/s, v_num=1, train_loss_step=549, train_loss_epoch=520]Epoch 216/400: 54%|█████▍ | 216/400 [00:38<00:32, 5.60it/s, v_num=1, train_loss_step=516, train_loss_epoch=520]Epoch 217/400: 54%|█████▍ | 216/400 [00:38<00:32, 5.60it/s, v_num=1, train_loss_step=516, train_loss_epoch=520]Epoch 217/400: 54%|█████▍ | 217/400 [00:38<00:31, 5.76it/s, v_num=1, train_loss_step=516, train_loss_epoch=520]Epoch 217/400: 54%|█████▍ | 217/400 [00:38<00:31, 5.76it/s, v_num=1, train_loss_step=514, train_loss_epoch=520]Epoch 218/400: 54%|█████▍ | 217/400 [00:38<00:31, 5.76it/s, v_num=1, train_loss_step=514, train_loss_epoch=520]Epoch 218/400: 55%|█████▍ | 218/400 [00:38<00:31, 5.74it/s, v_num=1, train_loss_step=514, train_loss_epoch=520]Epoch 218/400: 55%|█████▍ | 218/400 [00:38<00:31, 5.74it/s, v_num=1, train_loss_step=538, train_loss_epoch=520]Epoch 219/400: 55%|█████▍ | 218/400 [00:38<00:31, 5.74it/s, v_num=1, train_loss_step=538, train_loss_epoch=520]Epoch 219/400: 55%|█████▍ | 219/400 [00:38<00:31, 5.75it/s, v_num=1, train_loss_step=538, train_loss_epoch=520]Epoch 219/400: 55%|█████▍ | 219/400 [00:38<00:31, 5.75it/s, v_num=1, train_loss_step=501, train_loss_epoch=520]Epoch 220/400: 55%|█████▍ | 219/400 [00:38<00:31, 5.75it/s, v_num=1, train_loss_step=501, train_loss_epoch=520]Epoch 220/400: 55%|█████▌ | 220/400 [00:39<00:30, 5.86it/s, v_num=1, train_loss_step=501, train_loss_epoch=520]Epoch 220/400: 55%|█████▌ | 220/400 [00:39<00:30, 5.86it/s, v_num=1, train_loss_step=524, train_loss_epoch=520]Epoch 221/400: 55%|█████▌ | 220/400 [00:39<00:30, 5.86it/s, v_num=1, train_loss_step=524, train_loss_epoch=520]Epoch 221/400: 55%|█████▌ | 221/400 [00:39<00:31, 5.76it/s, v_num=1, train_loss_step=524, train_loss_epoch=520]Epoch 221/400: 55%|█████▌ | 221/400 [00:39<00:31, 5.76it/s, v_num=1, train_loss_step=512, train_loss_epoch=519]Epoch 222/400: 55%|█████▌ | 221/400 [00:39<00:31, 5.76it/s, v_num=1, train_loss_step=512, train_loss_epoch=519]Epoch 222/400: 56%|█████▌ | 222/400 [00:39<00:32, 5.56it/s, v_num=1, train_loss_step=512, train_loss_epoch=519]Epoch 222/400: 56%|█████▌ | 222/400 [00:39<00:32, 5.56it/s, v_num=1, train_loss_step=481, train_loss_epoch=519]Epoch 223/400: 56%|█████▌ | 222/400 [00:39<00:32, 5.56it/s, v_num=1, train_loss_step=481, train_loss_epoch=519]Epoch 223/400: 56%|█████▌ | 223/400 [00:39<00:32, 5.49it/s, v_num=1, train_loss_step=481, train_loss_epoch=519]Epoch 223/400: 56%|█████▌ | 223/400 [00:39<00:32, 5.49it/s, v_num=1, train_loss_step=516, train_loss_epoch=519]Epoch 224/400: 56%|█████▌ | 223/400 [00:39<00:32, 5.49it/s, v_num=1, train_loss_step=516, train_loss_epoch=519]Epoch 224/400: 56%|█████▌ | 224/400 [00:39<00:30, 5.74it/s, v_num=1, train_loss_step=516, train_loss_epoch=519]Epoch 224/400: 56%|█████▌ | 224/400 [00:39<00:30, 5.74it/s, v_num=1, train_loss_step=495, train_loss_epoch=519]Epoch 225/400: 56%|█████▌ | 224/400 [00:39<00:30, 5.74it/s, v_num=1, train_loss_step=495, train_loss_epoch=519]Epoch 225/400: 56%|█████▋ | 225/400 [00:39<00:29, 5.87it/s, v_num=1, train_loss_step=495, train_loss_epoch=519]Epoch 225/400: 56%|█████▋ | 225/400 [00:39<00:29, 5.87it/s, v_num=1, train_loss_step=537, train_loss_epoch=519]Epoch 226/400: 56%|█████▋ | 225/400 [00:39<00:29, 5.87it/s, v_num=1, train_loss_step=537, train_loss_epoch=519]Epoch 226/400: 56%|█████▋ | 226/400 [00:40<00:29, 5.96it/s, v_num=1, train_loss_step=537, train_loss_epoch=519]Epoch 226/400: 56%|█████▋ | 226/400 [00:40<00:29, 5.96it/s, v_num=1, train_loss_step=544, train_loss_epoch=519]Epoch 227/400: 56%|█████▋ | 226/400 [00:40<00:29, 5.96it/s, v_num=1, train_loss_step=544, train_loss_epoch=519]Epoch 227/400: 57%|█████▋ | 227/400 [00:40<00:28, 5.99it/s, v_num=1, train_loss_step=544, train_loss_epoch=519]Epoch 227/400: 57%|█████▋ | 227/400 [00:40<00:28, 5.99it/s, v_num=1, train_loss_step=573, train_loss_epoch=519]Epoch 228/400: 57%|█████▋ | 227/400 [00:40<00:28, 5.99it/s, v_num=1, train_loss_step=573, train_loss_epoch=519]Epoch 228/400: 57%|█████▋ | 228/400 [00:40<00:28, 6.05it/s, v_num=1, train_loss_step=573, train_loss_epoch=519]Epoch 228/400: 57%|█████▋ | 228/400 [00:40<00:28, 6.05it/s, v_num=1, train_loss_step=528, train_loss_epoch=519]Epoch 229/400: 57%|█████▋ | 228/400 [00:40<00:28, 6.05it/s, v_num=1, train_loss_step=528, train_loss_epoch=519]Epoch 229/400: 57%|█████▋ | 229/400 [00:40<00:27, 6.12it/s, v_num=1, train_loss_step=528, train_loss_epoch=519]Epoch 229/400: 57%|█████▋ | 229/400 [00:40<00:27, 6.12it/s, v_num=1, train_loss_step=500, train_loss_epoch=518]Epoch 230/400: 57%|█████▋ | 229/400 [00:40<00:27, 6.12it/s, v_num=1, train_loss_step=500, train_loss_epoch=518]Epoch 230/400: 57%|█████▊ | 230/400 [00:40<00:27, 6.22it/s, v_num=1, train_loss_step=500, train_loss_epoch=518]Epoch 230/400: 57%|█████▊ | 230/400 [00:40<00:27, 6.22it/s, v_num=1, train_loss_step=508, train_loss_epoch=519]Epoch 231/400: 57%|█████▊ | 230/400 [00:40<00:27, 6.22it/s, v_num=1, train_loss_step=508, train_loss_epoch=519]Epoch 231/400: 58%|█████▊ | 231/400 [00:40<00:27, 6.23it/s, v_num=1, train_loss_step=508, train_loss_epoch=519]Epoch 231/400: 58%|█████▊ | 231/400 [00:40<00:27, 6.23it/s, v_num=1, train_loss_step=536, train_loss_epoch=518]Epoch 232/400: 58%|█████▊ | 231/400 [00:40<00:27, 6.23it/s, v_num=1, train_loss_step=536, train_loss_epoch=518]Epoch 232/400: 58%|█████▊ | 232/400 [00:41<00:27, 6.16it/s, v_num=1, train_loss_step=536, train_loss_epoch=518]Epoch 232/400: 58%|█████▊ | 232/400 [00:41<00:27, 6.16it/s, v_num=1, train_loss_step=500, train_loss_epoch=518]Epoch 233/400: 58%|█████▊ | 232/400 [00:41<00:27, 6.16it/s, v_num=1, train_loss_step=500, train_loss_epoch=518]Epoch 233/400: 58%|█████▊ | 233/400 [00:41<00:27, 6.07it/s, v_num=1, train_loss_step=500, train_loss_epoch=518]Epoch 233/400: 58%|█████▊ | 233/400 [00:41<00:27, 6.07it/s, v_num=1, train_loss_step=509, train_loss_epoch=518]Epoch 234/400: 58%|█████▊ | 233/400 [00:41<00:27, 6.07it/s, v_num=1, train_loss_step=509, train_loss_epoch=518]Epoch 234/400: 58%|█████▊ | 234/400 [00:41<00:28, 5.92it/s, v_num=1, train_loss_step=509, train_loss_epoch=518]Epoch 234/400: 58%|█████▊ | 234/400 [00:41<00:28, 5.92it/s, v_num=1, train_loss_step=531, train_loss_epoch=518]Epoch 235/400: 58%|█████▊ | 234/400 [00:41<00:28, 5.92it/s, v_num=1, train_loss_step=531, train_loss_epoch=518]Epoch 235/400: 59%|█████▉ | 235/400 [00:41<00:29, 5.64it/s, v_num=1, train_loss_step=531, train_loss_epoch=518]Epoch 235/400: 59%|█████▉ | 235/400 [00:41<00:29, 5.64it/s, v_num=1, train_loss_step=516, train_loss_epoch=517]Epoch 236/400: 59%|█████▉ | 235/400 [00:41<00:29, 5.64it/s, v_num=1, train_loss_step=516, train_loss_epoch=517]Epoch 236/400: 59%|█████▉ | 236/400 [00:41<00:28, 5.67it/s, v_num=1, train_loss_step=516, train_loss_epoch=517]Epoch 236/400: 59%|█████▉ | 236/400 [00:41<00:28, 5.67it/s, v_num=1, train_loss_step=523, train_loss_epoch=517]Epoch 237/400: 59%|█████▉ | 236/400 [00:41<00:28, 5.67it/s, v_num=1, train_loss_step=523, train_loss_epoch=517]Epoch 237/400: 59%|█████▉ | 237/400 [00:41<00:28, 5.78it/s, v_num=1, train_loss_step=523, train_loss_epoch=517]Epoch 237/400: 59%|█████▉ | 237/400 [00:41<00:28, 5.78it/s, v_num=1, train_loss_step=508, train_loss_epoch=518]Epoch 238/400: 59%|█████▉ | 237/400 [00:41<00:28, 5.78it/s, v_num=1, train_loss_step=508, train_loss_epoch=518]Epoch 238/400: 60%|█████▉ | 238/400 [00:42<00:27, 5.85it/s, v_num=1, train_loss_step=508, train_loss_epoch=518]Epoch 238/400: 60%|█████▉ | 238/400 [00:42<00:27, 5.85it/s, v_num=1, train_loss_step=526, train_loss_epoch=517]Epoch 239/400: 60%|█████▉ | 238/400 [00:42<00:27, 5.85it/s, v_num=1, train_loss_step=526, train_loss_epoch=517]Epoch 239/400: 60%|█████▉ | 239/400 [00:42<00:27, 5.93it/s, v_num=1, train_loss_step=526, train_loss_epoch=517]Epoch 239/400: 60%|█████▉ | 239/400 [00:42<00:27, 5.93it/s, v_num=1, train_loss_step=514, train_loss_epoch=517]Epoch 240/400: 60%|█████▉ | 239/400 [00:42<00:27, 5.93it/s, v_num=1, train_loss_step=514, train_loss_epoch=517]Epoch 240/400: 60%|██████ | 240/400 [00:42<00:26, 6.04it/s, v_num=1, train_loss_step=514, train_loss_epoch=517]Epoch 240/400: 60%|██████ | 240/400 [00:42<00:26, 6.04it/s, v_num=1, train_loss_step=518, train_loss_epoch=517]Epoch 241/400: 60%|██████ | 240/400 [00:42<00:26, 6.04it/s, v_num=1, train_loss_step=518, train_loss_epoch=517]Epoch 241/400: 60%|██████ | 241/400 [00:42<00:25, 6.16it/s, v_num=1, train_loss_step=518, train_loss_epoch=517]Epoch 241/400: 60%|██████ | 241/400 [00:42<00:25, 6.16it/s, v_num=1, train_loss_step=501, train_loss_epoch=517]Epoch 242/400: 60%|██████ | 241/400 [00:42<00:25, 6.16it/s, v_num=1, train_loss_step=501, train_loss_epoch=517]Epoch 242/400: 60%|██████ | 242/400 [00:42<00:26, 5.86it/s, v_num=1, train_loss_step=501, train_loss_epoch=517]Epoch 242/400: 60%|██████ | 242/400 [00:42<00:26, 5.86it/s, v_num=1, train_loss_step=503, train_loss_epoch=517]Epoch 243/400: 60%|██████ | 242/400 [00:42<00:26, 5.86it/s, v_num=1, train_loss_step=503, train_loss_epoch=517]Epoch 243/400: 61%|██████ | 243/400 [00:42<00:27, 5.74it/s, v_num=1, train_loss_step=503, train_loss_epoch=517]Epoch 243/400: 61%|██████ | 243/400 [00:42<00:27, 5.74it/s, v_num=1, train_loss_step=526, train_loss_epoch=517]Epoch 244/400: 61%|██████ | 243/400 [00:43<00:27, 5.74it/s, v_num=1, train_loss_step=526, train_loss_epoch=517]Epoch 244/400: 61%|██████ | 244/400 [00:43<00:26, 5.85it/s, v_num=1, train_loss_step=526, train_loss_epoch=517]Epoch 244/400: 61%|██████ | 244/400 [00:43<00:26, 5.85it/s, v_num=1, train_loss_step=514, train_loss_epoch=517]Epoch 245/400: 61%|██████ | 244/400 [00:43<00:26, 5.85it/s, v_num=1, train_loss_step=514, train_loss_epoch=517]Epoch 245/400: 61%|██████▏ | 245/400 [00:43<00:27, 5.59it/s, v_num=1, train_loss_step=514, train_loss_epoch=517]Epoch 245/400: 61%|██████▏ | 245/400 [00:43<00:27, 5.59it/s, v_num=1, train_loss_step=506, train_loss_epoch=517]Epoch 246/400: 61%|██████▏ | 245/400 [00:43<00:27, 5.59it/s, v_num=1, train_loss_step=506, train_loss_epoch=517]Epoch 246/400: 62%|██████▏ | 246/400 [00:43<00:26, 5.74it/s, v_num=1, train_loss_step=506, train_loss_epoch=517]Epoch 246/400: 62%|██████▏ | 246/400 [00:43<00:26, 5.74it/s, v_num=1, train_loss_step=512, train_loss_epoch=516]Epoch 247/400: 62%|██████▏ | 246/400 [00:43<00:26, 5.74it/s, v_num=1, train_loss_step=512, train_loss_epoch=516]Epoch 247/400: 62%|██████▏ | 247/400 [00:43<00:27, 5.55it/s, v_num=1, train_loss_step=512, train_loss_epoch=516]Epoch 247/400: 62%|██████▏ | 247/400 [00:43<00:27, 5.55it/s, v_num=1, train_loss_step=527, train_loss_epoch=517]Epoch 248/400: 62%|██████▏ | 247/400 [00:43<00:27, 5.55it/s, v_num=1, train_loss_step=527, train_loss_epoch=517]Epoch 248/400: 62%|██████▏ | 248/400 [00:43<00:26, 5.70it/s, v_num=1, train_loss_step=527, train_loss_epoch=517]Epoch 248/400: 62%|██████▏ | 248/400 [00:43<00:26, 5.70it/s, v_num=1, train_loss_step=471, train_loss_epoch=516]Epoch 249/400: 62%|██████▏ | 248/400 [00:43<00:26, 5.70it/s, v_num=1, train_loss_step=471, train_loss_epoch=516]Epoch 249/400: 62%|██████▏ | 249/400 [00:44<00:25, 5.81it/s, v_num=1, train_loss_step=471, train_loss_epoch=516]Epoch 249/400: 62%|██████▏ | 249/400 [00:44<00:25, 5.81it/s, v_num=1, train_loss_step=515, train_loss_epoch=516]Epoch 250/400: 62%|██████▏ | 249/400 [00:44<00:25, 5.81it/s, v_num=1, train_loss_step=515, train_loss_epoch=516]Epoch 250/400: 62%|██████▎ | 250/400 [00:44<00:26, 5.64it/s, v_num=1, train_loss_step=515, train_loss_epoch=516]Epoch 250/400: 62%|██████▎ | 250/400 [00:44<00:26, 5.64it/s, v_num=1, train_loss_step=506, train_loss_epoch=516]Epoch 251/400: 62%|██████▎ | 250/400 [00:44<00:26, 5.64it/s, v_num=1, train_loss_step=506, train_loss_epoch=516]Epoch 251/400: 63%|██████▎ | 251/400 [00:44<00:27, 5.48it/s, v_num=1, train_loss_step=506, train_loss_epoch=516]Epoch 251/400: 63%|██████▎ | 251/400 [00:44<00:27, 5.48it/s, v_num=1, train_loss_step=530, train_loss_epoch=516]Epoch 252/400: 63%|██████▎ | 251/400 [00:44<00:27, 5.48it/s, v_num=1, train_loss_step=530, train_loss_epoch=516]Epoch 252/400: 63%|██████▎ | 252/400 [00:44<00:26, 5.50it/s, v_num=1, train_loss_step=530, train_loss_epoch=516]Epoch 252/400: 63%|██████▎ | 252/400 [00:44<00:26, 5.50it/s, v_num=1, train_loss_step=538, train_loss_epoch=516]Epoch 253/400: 63%|██████▎ | 252/400 [00:44<00:26, 5.50it/s, v_num=1, train_loss_step=538, train_loss_epoch=516]Epoch 253/400: 63%|██████▎ | 253/400 [00:44<00:25, 5.73it/s, v_num=1, train_loss_step=538, train_loss_epoch=516]Epoch 253/400: 63%|██████▎ | 253/400 [00:44<00:25, 5.73it/s, v_num=1, train_loss_step=525, train_loss_epoch=516]Epoch 254/400: 63%|██████▎ | 253/400 [00:44<00:25, 5.73it/s, v_num=1, train_loss_step=525, train_loss_epoch=516]Epoch 254/400: 64%|██████▎ | 254/400 [00:44<00:26, 5.57it/s, v_num=1, train_loss_step=525, train_loss_epoch=516]Epoch 254/400: 64%|██████▎ | 254/400 [00:44<00:26, 5.57it/s, v_num=1, train_loss_step=533, train_loss_epoch=516]Epoch 255/400: 64%|██████▎ | 254/400 [00:44<00:26, 5.57it/s, v_num=1, train_loss_step=533, train_loss_epoch=516]Epoch 255/400: 64%|██████▍ | 255/400 [00:45<00:26, 5.38it/s, v_num=1, train_loss_step=533, train_loss_epoch=516]Epoch 255/400: 64%|██████▍ | 255/400 [00:45<00:26, 5.38it/s, v_num=1, train_loss_step=526, train_loss_epoch=516]Epoch 256/400: 64%|██████▍ | 255/400 [00:45<00:26, 5.38it/s, v_num=1, train_loss_step=526, train_loss_epoch=516]Epoch 256/400: 64%|██████▍ | 256/400 [00:45<00:27, 5.32it/s, v_num=1, train_loss_step=526, train_loss_epoch=516]Epoch 256/400: 64%|██████▍ | 256/400 [00:45<00:27, 5.32it/s, v_num=1, train_loss_step=522, train_loss_epoch=515]Epoch 257/400: 64%|██████▍ | 256/400 [00:45<00:27, 5.32it/s, v_num=1, train_loss_step=522, train_loss_epoch=515]Epoch 257/400: 64%|██████▍ | 257/400 [00:45<00:27, 5.25it/s, v_num=1, train_loss_step=522, train_loss_epoch=515]Epoch 257/400: 64%|██████▍ | 257/400 [00:45<00:27, 5.25it/s, v_num=1, train_loss_step=543, train_loss_epoch=515]Epoch 258/400: 64%|██████▍ | 257/400 [00:45<00:27, 5.25it/s, v_num=1, train_loss_step=543, train_loss_epoch=515]Epoch 258/400: 64%|██████▍ | 258/400 [00:45<00:26, 5.38it/s, v_num=1, train_loss_step=543, train_loss_epoch=515]Epoch 258/400: 64%|██████▍ | 258/400 [00:45<00:26, 5.38it/s, v_num=1, train_loss_step=526, train_loss_epoch=515]Epoch 259/400: 64%|██████▍ | 258/400 [00:45<00:26, 5.38it/s, v_num=1, train_loss_step=526, train_loss_epoch=515]Epoch 259/400: 65%|██████▍ | 259/400 [00:45<00:25, 5.54it/s, v_num=1, train_loss_step=526, train_loss_epoch=515]Epoch 259/400: 65%|██████▍ | 259/400 [00:45<00:25, 5.54it/s, v_num=1, train_loss_step=502, train_loss_epoch=515]Epoch 260/400: 65%|██████▍ | 259/400 [00:45<00:25, 5.54it/s, v_num=1, train_loss_step=502, train_loss_epoch=515]Epoch 260/400: 65%|██████▌ | 260/400 [00:46<00:24, 5.75it/s, v_num=1, train_loss_step=502, train_loss_epoch=515]Epoch 260/400: 65%|██████▌ | 260/400 [00:46<00:24, 5.75it/s, v_num=1, train_loss_step=556, train_loss_epoch=515]Epoch 261/400: 65%|██████▌ | 260/400 [00:46<00:24, 5.75it/s, v_num=1, train_loss_step=556, train_loss_epoch=515]Epoch 261/400: 65%|██████▌ | 261/400 [00:46<00:23, 5.94it/s, v_num=1, train_loss_step=556, train_loss_epoch=515]Epoch 261/400: 65%|██████▌ | 261/400 [00:46<00:23, 5.94it/s, v_num=1, train_loss_step=544, train_loss_epoch=515]Epoch 262/400: 65%|██████▌ | 261/400 [00:46<00:23, 5.94it/s, v_num=1, train_loss_step=544, train_loss_epoch=515]Epoch 262/400: 66%|██████▌ | 262/400 [00:46<00:23, 5.93it/s, v_num=1, train_loss_step=544, train_loss_epoch=515]Epoch 262/400: 66%|██████▌ | 262/400 [00:46<00:23, 5.93it/s, v_num=1, train_loss_step=505, train_loss_epoch=515]Epoch 263/400: 66%|██████▌ | 262/400 [00:46<00:23, 5.93it/s, v_num=1, train_loss_step=505, train_loss_epoch=515]Epoch 263/400: 66%|██████▌ | 263/400 [00:46<00:24, 5.70it/s, v_num=1, train_loss_step=505, train_loss_epoch=515]Epoch 263/400: 66%|██████▌ | 263/400 [00:46<00:24, 5.70it/s, v_num=1, train_loss_step=490, train_loss_epoch=515]Epoch 264/400: 66%|██████▌ | 263/400 [00:46<00:24, 5.70it/s, v_num=1, train_loss_step=490, train_loss_epoch=515]Epoch 264/400: 66%|██████▌ | 264/400 [00:46<00:23, 5.77it/s, v_num=1, train_loss_step=490, train_loss_epoch=515]Epoch 264/400: 66%|██████▌ | 264/400 [00:46<00:23, 5.77it/s, v_num=1, train_loss_step=543, train_loss_epoch=515]Epoch 265/400: 66%|██████▌ | 264/400 [00:46<00:23, 5.77it/s, v_num=1, train_loss_step=543, train_loss_epoch=515]Epoch 265/400: 66%|██████▋ | 265/400 [00:46<00:23, 5.81it/s, v_num=1, train_loss_step=543, train_loss_epoch=515]Epoch 265/400: 66%|██████▋ | 265/400 [00:46<00:23, 5.81it/s, v_num=1, train_loss_step=512, train_loss_epoch=515]Epoch 266/400: 66%|██████▋ | 265/400 [00:46<00:23, 5.81it/s, v_num=1, train_loss_step=512, train_loss_epoch=515]Epoch 266/400: 66%|██████▋ | 266/400 [00:47<00:22, 5.94it/s, v_num=1, train_loss_step=512, train_loss_epoch=515]Epoch 266/400: 66%|██████▋ | 266/400 [00:47<00:22, 5.94it/s, v_num=1, train_loss_step=485, train_loss_epoch=515]Epoch 267/400: 66%|██████▋ | 266/400 [00:47<00:22, 5.94it/s, v_num=1, train_loss_step=485, train_loss_epoch=515]Epoch 267/400: 67%|██████▋ | 267/400 [00:47<00:22, 6.03it/s, v_num=1, train_loss_step=485, train_loss_epoch=515]Epoch 267/400: 67%|██████▋ | 267/400 [00:47<00:22, 6.03it/s, v_num=1, train_loss_step=511, train_loss_epoch=515]Epoch 268/400: 67%|██████▋ | 267/400 [00:47<00:22, 6.03it/s, v_num=1, train_loss_step=511, train_loss_epoch=515]Epoch 268/400: 67%|██████▋ | 268/400 [00:47<00:21, 6.06it/s, v_num=1, train_loss_step=511, train_loss_epoch=515]Epoch 268/400: 67%|██████▋ | 268/400 [00:47<00:21, 6.06it/s, v_num=1, train_loss_step=503, train_loss_epoch=514]Epoch 269/400: 67%|██████▋ | 268/400 [00:47<00:21, 6.06it/s, v_num=1, train_loss_step=503, train_loss_epoch=514]Epoch 269/400: 67%|██████▋ | 269/400 [00:47<00:21, 6.07it/s, v_num=1, train_loss_step=503, train_loss_epoch=514]Epoch 269/400: 67%|██████▋ | 269/400 [00:47<00:21, 6.07it/s, v_num=1, train_loss_step=495, train_loss_epoch=514]Epoch 270/400: 67%|██████▋ | 269/400 [00:47<00:21, 6.07it/s, v_num=1, train_loss_step=495, train_loss_epoch=514]Epoch 270/400: 68%|██████▊ | 270/400 [00:47<00:21, 6.07it/s, v_num=1, train_loss_step=495, train_loss_epoch=514]Epoch 270/400: 68%|██████▊ | 270/400 [00:47<00:21, 6.07it/s, v_num=1, train_loss_step=534, train_loss_epoch=514]Epoch 271/400: 68%|██████▊ | 270/400 [00:47<00:21, 6.07it/s, v_num=1, train_loss_step=534, train_loss_epoch=514]Epoch 271/400: 68%|██████▊ | 271/400 [00:47<00:20, 6.15it/s, v_num=1, train_loss_step=534, train_loss_epoch=514]Epoch 271/400: 68%|██████▊ | 271/400 [00:47<00:20, 6.15it/s, v_num=1, train_loss_step=512, train_loss_epoch=514]Epoch 272/400: 68%|██████▊ | 271/400 [00:47<00:20, 6.15it/s, v_num=1, train_loss_step=512, train_loss_epoch=514]Epoch 272/400: 68%|██████▊ | 272/400 [00:48<00:20, 6.17it/s, v_num=1, train_loss_step=512, train_loss_epoch=514]Epoch 272/400: 68%|██████▊ | 272/400 [00:48<00:20, 6.17it/s, v_num=1, train_loss_step=535, train_loss_epoch=514]Epoch 273/400: 68%|██████▊ | 272/400 [00:48<00:20, 6.17it/s, v_num=1, train_loss_step=535, train_loss_epoch=514]Epoch 273/400: 68%|██████▊ | 273/400 [00:48<00:20, 6.20it/s, v_num=1, train_loss_step=535, train_loss_epoch=514]Epoch 273/400: 68%|██████▊ | 273/400 [00:48<00:20, 6.20it/s, v_num=1, train_loss_step=526, train_loss_epoch=514]Epoch 274/400: 68%|██████▊ | 273/400 [00:48<00:20, 6.20it/s, v_num=1, train_loss_step=526, train_loss_epoch=514]Epoch 274/400: 68%|██████▊ | 274/400 [00:48<00:20, 6.17it/s, v_num=1, train_loss_step=526, train_loss_epoch=514]Epoch 274/400: 68%|██████▊ | 274/400 [00:48<00:20, 6.17it/s, v_num=1, train_loss_step=520, train_loss_epoch=514]Epoch 275/400: 68%|██████▊ | 274/400 [00:48<00:20, 6.17it/s, v_num=1, train_loss_step=520, train_loss_epoch=514]Epoch 275/400: 69%|██████▉ | 275/400 [00:48<00:20, 6.13it/s, v_num=1, train_loss_step=520, train_loss_epoch=514]Epoch 275/400: 69%|██████▉ | 275/400 [00:48<00:20, 6.13it/s, v_num=1, train_loss_step=512, train_loss_epoch=514]Epoch 276/400: 69%|██████▉ | 275/400 [00:48<00:20, 6.13it/s, v_num=1, train_loss_step=512, train_loss_epoch=514]Epoch 276/400: 69%|██████▉ | 276/400 [00:48<00:20, 6.11it/s, v_num=1, train_loss_step=512, train_loss_epoch=514]Epoch 276/400: 69%|██████▉ | 276/400 [00:48<00:20, 6.11it/s, v_num=1, train_loss_step=496, train_loss_epoch=514]Epoch 277/400: 69%|██████▉ | 276/400 [00:48<00:20, 6.11it/s, v_num=1, train_loss_step=496, train_loss_epoch=514]Epoch 277/400: 69%|██████▉ | 277/400 [00:48<00:21, 5.65it/s, v_num=1, train_loss_step=496, train_loss_epoch=514]Epoch 277/400: 69%|██████▉ | 277/400 [00:48<00:21, 5.65it/s, v_num=1, train_loss_step=522, train_loss_epoch=513]Epoch 278/400: 69%|██████▉ | 277/400 [00:48<00:21, 5.65it/s, v_num=1, train_loss_step=522, train_loss_epoch=513]Epoch 278/400: 70%|██████▉ | 278/400 [00:49<00:22, 5.41it/s, v_num=1, train_loss_step=522, train_loss_epoch=513]Epoch 278/400: 70%|██████▉ | 278/400 [00:49<00:22, 5.41it/s, v_num=1, train_loss_step=512, train_loss_epoch=513]Epoch 279/400: 70%|██████▉ | 278/400 [00:49<00:22, 5.41it/s, v_num=1, train_loss_step=512, train_loss_epoch=513]Epoch 279/400: 70%|██████▉ | 279/400 [00:49<00:22, 5.36it/s, v_num=1, train_loss_step=512, train_loss_epoch=513]Epoch 279/400: 70%|██████▉ | 279/400 [00:49<00:22, 5.36it/s, v_num=1, train_loss_step=493, train_loss_epoch=514]Epoch 280/400: 70%|██████▉ | 279/400 [00:49<00:22, 5.36it/s, v_num=1, train_loss_step=493, train_loss_epoch=514]Epoch 280/400: 70%|███████ | 280/400 [00:49<00:21, 5.50it/s, v_num=1, train_loss_step=493, train_loss_epoch=514]Epoch 280/400: 70%|███████ | 280/400 [00:49<00:21, 5.50it/s, v_num=1, train_loss_step=512, train_loss_epoch=513]Epoch 281/400: 70%|███████ | 280/400 [00:49<00:21, 5.50it/s, v_num=1, train_loss_step=512, train_loss_epoch=513]Epoch 281/400: 70%|███████ | 281/400 [00:49<00:22, 5.41it/s, v_num=1, train_loss_step=512, train_loss_epoch=513]Epoch 281/400: 70%|███████ | 281/400 [00:49<00:22, 5.41it/s, v_num=1, train_loss_step=501, train_loss_epoch=513]Epoch 282/400: 70%|███████ | 281/400 [00:49<00:22, 5.41it/s, v_num=1, train_loss_step=501, train_loss_epoch=513]Epoch 282/400: 70%|███████ | 282/400 [00:49<00:22, 5.24it/s, v_num=1, train_loss_step=501, train_loss_epoch=513]Epoch 282/400: 70%|███████ | 282/400 [00:49<00:22, 5.24it/s, v_num=1, train_loss_step=536, train_loss_epoch=513]Epoch 283/400: 70%|███████ | 282/400 [00:49<00:22, 5.24it/s, v_num=1, train_loss_step=536, train_loss_epoch=513]Epoch 283/400: 71%|███████ | 283/400 [00:50<00:22, 5.23it/s, v_num=1, train_loss_step=536, train_loss_epoch=513]Epoch 283/400: 71%|███████ | 283/400 [00:50<00:22, 5.23it/s, v_num=1, train_loss_step=528, train_loss_epoch=513]Epoch 284/400: 71%|███████ | 283/400 [00:50<00:22, 5.23it/s, v_num=1, train_loss_step=528, train_loss_epoch=513]Epoch 284/400: 71%|███████ | 284/400 [00:50<00:21, 5.41it/s, v_num=1, train_loss_step=528, train_loss_epoch=513]Epoch 284/400: 71%|███████ | 284/400 [00:50<00:21, 5.41it/s, v_num=1, train_loss_step=538, train_loss_epoch=513]Epoch 285/400: 71%|███████ | 284/400 [00:50<00:21, 5.41it/s, v_num=1, train_loss_step=538, train_loss_epoch=513]Epoch 285/400: 71%|███████▏ | 285/400 [00:50<00:21, 5.46it/s, v_num=1, train_loss_step=538, train_loss_epoch=513]Epoch 285/400: 71%|███████▏ | 285/400 [00:50<00:21, 5.46it/s, v_num=1, train_loss_step=528, train_loss_epoch=513]Epoch 286/400: 71%|███████▏ | 285/400 [00:50<00:21, 5.46it/s, v_num=1, train_loss_step=528, train_loss_epoch=513]Epoch 286/400: 72%|███████▏ | 286/400 [00:50<00:20, 5.56it/s, v_num=1, train_loss_step=528, train_loss_epoch=513]Epoch 286/400: 72%|███████▏ | 286/400 [00:50<00:20, 5.56it/s, v_num=1, train_loss_step=536, train_loss_epoch=513]Epoch 287/400: 72%|███████▏ | 286/400 [00:50<00:20, 5.56it/s, v_num=1, train_loss_step=536, train_loss_epoch=513]Epoch 287/400: 72%|███████▏ | 287/400 [00:50<00:21, 5.37it/s, v_num=1, train_loss_step=536, train_loss_epoch=513]Epoch 287/400: 72%|███████▏ | 287/400 [00:50<00:21, 5.37it/s, v_num=1, train_loss_step=549, train_loss_epoch=512]Epoch 288/400: 72%|███████▏ | 287/400 [00:50<00:21, 5.37it/s, v_num=1, train_loss_step=549, train_loss_epoch=512]Epoch 288/400: 72%|███████▏ | 288/400 [00:50<00:21, 5.20it/s, v_num=1, train_loss_step=549, train_loss_epoch=512]Epoch 288/400: 72%|███████▏ | 288/400 [00:50<00:21, 5.20it/s, v_num=1, train_loss_step=505, train_loss_epoch=513]Epoch 289/400: 72%|███████▏ | 288/400 [00:50<00:21, 5.20it/s, v_num=1, train_loss_step=505, train_loss_epoch=513]Epoch 289/400: 72%|███████▏ | 289/400 [00:51<00:21, 5.22it/s, v_num=1, train_loss_step=505, train_loss_epoch=513]Epoch 289/400: 72%|███████▏ | 289/400 [00:51<00:21, 5.22it/s, v_num=1, train_loss_step=513, train_loss_epoch=512]Epoch 290/400: 72%|███████▏ | 289/400 [00:51<00:21, 5.22it/s, v_num=1, train_loss_step=513, train_loss_epoch=512]Epoch 290/400: 72%|███████▎ | 290/400 [00:51<00:20, 5.32it/s, v_num=1, train_loss_step=513, train_loss_epoch=512]Epoch 290/400: 72%|███████▎ | 290/400 [00:51<00:20, 5.32it/s, v_num=1, train_loss_step=513, train_loss_epoch=513]Epoch 291/400: 72%|███████▎ | 290/400 [00:51<00:20, 5.32it/s, v_num=1, train_loss_step=513, train_loss_epoch=513]Epoch 291/400: 73%|███████▎ | 291/400 [00:51<00:19, 5.46it/s, v_num=1, train_loss_step=513, train_loss_epoch=513]Epoch 291/400: 73%|███████▎ | 291/400 [00:51<00:19, 5.46it/s, v_num=1, train_loss_step=549, train_loss_epoch=512]Epoch 292/400: 73%|███████▎ | 291/400 [00:51<00:19, 5.46it/s, v_num=1, train_loss_step=549, train_loss_epoch=512]Epoch 292/400: 73%|███████▎ | 292/400 [00:51<00:19, 5.45it/s, v_num=1, train_loss_step=549, train_loss_epoch=512]Epoch 292/400: 73%|███████▎ | 292/400 [00:51<00:19, 5.45it/s, v_num=1, train_loss_step=492, train_loss_epoch=512]Epoch 293/400: 73%|███████▎ | 292/400 [00:51<00:19, 5.45it/s, v_num=1, train_loss_step=492, train_loss_epoch=512]Epoch 293/400: 73%|███████▎ | 293/400 [00:51<00:19, 5.43it/s, v_num=1, train_loss_step=492, train_loss_epoch=512]Epoch 293/400: 73%|███████▎ | 293/400 [00:51<00:19, 5.43it/s, v_num=1, train_loss_step=491, train_loss_epoch=512]Epoch 294/400: 73%|███████▎ | 293/400 [00:51<00:19, 5.43it/s, v_num=1, train_loss_step=491, train_loss_epoch=512]Epoch 294/400: 74%|███████▎ | 294/400 [00:52<00:19, 5.39it/s, v_num=1, train_loss_step=491, train_loss_epoch=512]Epoch 294/400: 74%|███████▎ | 294/400 [00:52<00:19, 5.39it/s, v_num=1, train_loss_step=528, train_loss_epoch=512]Epoch 295/400: 74%|███████▎ | 294/400 [00:52<00:19, 5.39it/s, v_num=1, train_loss_step=528, train_loss_epoch=512]Epoch 295/400: 74%|███████▍ | 295/400 [00:52<00:18, 5.59it/s, v_num=1, train_loss_step=528, train_loss_epoch=512]Epoch 295/400: 74%|███████▍ | 295/400 [00:52<00:18, 5.59it/s, v_num=1, train_loss_step=540, train_loss_epoch=512]Epoch 296/400: 74%|███████▍ | 295/400 [00:52<00:18, 5.59it/s, v_num=1, train_loss_step=540, train_loss_epoch=512]Epoch 296/400: 74%|███████▍ | 296/400 [00:52<00:18, 5.78it/s, v_num=1, train_loss_step=540, train_loss_epoch=512]Epoch 296/400: 74%|███████▍ | 296/400 [00:52<00:18, 5.78it/s, v_num=1, train_loss_step=544, train_loss_epoch=513]Epoch 297/400: 74%|███████▍ | 296/400 [00:52<00:18, 5.78it/s, v_num=1, train_loss_step=544, train_loss_epoch=513]Epoch 297/400: 74%|███████▍ | 297/400 [00:52<00:17, 5.90it/s, v_num=1, train_loss_step=544, train_loss_epoch=513]Epoch 297/400: 74%|███████▍ | 297/400 [00:52<00:17, 5.90it/s, v_num=1, train_loss_step=489, train_loss_epoch=512]Epoch 298/400: 74%|███████▍ | 297/400 [00:52<00:17, 5.90it/s, v_num=1, train_loss_step=489, train_loss_epoch=512]Epoch 298/400: 74%|███████▍ | 298/400 [00:52<00:17, 5.96it/s, v_num=1, train_loss_step=489, train_loss_epoch=512]Epoch 298/400: 74%|███████▍ | 298/400 [00:52<00:17, 5.96it/s, v_num=1, train_loss_step=511, train_loss_epoch=512]Epoch 299/400: 74%|███████▍ | 298/400 [00:52<00:17, 5.96it/s, v_num=1, train_loss_step=511, train_loss_epoch=512]Epoch 299/400: 75%|███████▍ | 299/400 [00:52<00:17, 5.88it/s, v_num=1, train_loss_step=511, train_loss_epoch=512]Epoch 299/400: 75%|███████▍ | 299/400 [00:52<00:17, 5.88it/s, v_num=1, train_loss_step=518, train_loss_epoch=513]Epoch 300/400: 75%|███████▍ | 299/400 [00:52<00:17, 5.88it/s, v_num=1, train_loss_step=518, train_loss_epoch=513]Epoch 300/400: 75%|███████▌ | 300/400 [00:53<00:18, 5.50it/s, v_num=1, train_loss_step=518, train_loss_epoch=513]Epoch 300/400: 75%|███████▌ | 300/400 [00:53<00:18, 5.50it/s, v_num=1, train_loss_step=517, train_loss_epoch=512]Epoch 301/400: 75%|███████▌ | 300/400 [00:53<00:18, 5.50it/s, v_num=1, train_loss_step=517, train_loss_epoch=512]Epoch 301/400: 75%|███████▌ | 301/400 [00:53<00:17, 5.60it/s, v_num=1, train_loss_step=517, train_loss_epoch=512]Epoch 301/400: 75%|███████▌ | 301/400 [00:53<00:17, 5.60it/s, v_num=1, train_loss_step=550, train_loss_epoch=512]Epoch 302/400: 75%|███████▌ | 301/400 [00:53<00:17, 5.60it/s, v_num=1, train_loss_step=550, train_loss_epoch=512]Epoch 302/400: 76%|███████▌ | 302/400 [00:53<00:17, 5.75it/s, v_num=1, train_loss_step=550, train_loss_epoch=512]Epoch 302/400: 76%|███████▌ | 302/400 [00:53<00:17, 5.75it/s, v_num=1, train_loss_step=522, train_loss_epoch=512]Epoch 303/400: 76%|███████▌ | 302/400 [00:53<00:17, 5.75it/s, v_num=1, train_loss_step=522, train_loss_epoch=512]Epoch 303/400: 76%|███████▌ | 303/400 [00:53<00:16, 5.79it/s, v_num=1, train_loss_step=522, train_loss_epoch=512]Epoch 303/400: 76%|███████▌ | 303/400 [00:53<00:16, 5.79it/s, v_num=1, train_loss_step=510, train_loss_epoch=511]Epoch 304/400: 76%|███████▌ | 303/400 [00:53<00:16, 5.79it/s, v_num=1, train_loss_step=510, train_loss_epoch=511]Epoch 304/400: 76%|███████▌ | 304/400 [00:53<00:16, 5.86it/s, v_num=1, train_loss_step=510, train_loss_epoch=511]Epoch 304/400: 76%|███████▌ | 304/400 [00:53<00:16, 5.86it/s, v_num=1, train_loss_step=492, train_loss_epoch=512]Epoch 305/400: 76%|███████▌ | 304/400 [00:53<00:16, 5.86it/s, v_num=1, train_loss_step=492, train_loss_epoch=512]Epoch 305/400: 76%|███████▋ | 305/400 [00:53<00:16, 5.60it/s, v_num=1, train_loss_step=492, train_loss_epoch=512]Epoch 305/400: 76%|███████▋ | 305/400 [00:53<00:16, 5.60it/s, v_num=1, train_loss_step=489, train_loss_epoch=512]Epoch 306/400: 76%|███████▋ | 305/400 [00:53<00:16, 5.60it/s, v_num=1, train_loss_step=489, train_loss_epoch=512]Epoch 306/400: 76%|███████▋ | 306/400 [00:54<00:17, 5.42it/s, v_num=1, train_loss_step=489, train_loss_epoch=512]Epoch 306/400: 76%|███████▋ | 306/400 [00:54<00:17, 5.42it/s, v_num=1, train_loss_step=505, train_loss_epoch=511]Epoch 307/400: 76%|███████▋ | 306/400 [00:54<00:17, 5.42it/s, v_num=1, train_loss_step=505, train_loss_epoch=511]Epoch 307/400: 77%|███████▋ | 307/400 [00:54<00:17, 5.25it/s, v_num=1, train_loss_step=505, train_loss_epoch=511]Epoch 307/400: 77%|███████▋ | 307/400 [00:54<00:17, 5.25it/s, v_num=1, train_loss_step=533, train_loss_epoch=511]Epoch 308/400: 77%|███████▋ | 307/400 [00:54<00:17, 5.25it/s, v_num=1, train_loss_step=533, train_loss_epoch=511]Epoch 308/400: 77%|███████▋ | 308/400 [00:54<00:17, 5.16it/s, v_num=1, train_loss_step=533, train_loss_epoch=511]Epoch 308/400: 77%|███████▋ | 308/400 [00:54<00:17, 5.16it/s, v_num=1, train_loss_step=498, train_loss_epoch=511]Epoch 309/400: 77%|███████▋ | 308/400 [00:54<00:17, 5.16it/s, v_num=1, train_loss_step=498, train_loss_epoch=511]Epoch 309/400: 77%|███████▋ | 309/400 [00:54<00:17, 5.21it/s, v_num=1, train_loss_step=498, train_loss_epoch=511]Epoch 309/400: 77%|███████▋ | 309/400 [00:54<00:17, 5.21it/s, v_num=1, train_loss_step=520, train_loss_epoch=512]Epoch 310/400: 77%|███████▋ | 309/400 [00:54<00:17, 5.21it/s, v_num=1, train_loss_step=520, train_loss_epoch=512]Epoch 310/400: 78%|███████▊ | 310/400 [00:54<00:16, 5.34it/s, v_num=1, train_loss_step=520, train_loss_epoch=512]Epoch 310/400: 78%|███████▊ | 310/400 [00:54<00:16, 5.34it/s, v_num=1, train_loss_step=511, train_loss_epoch=511]Epoch 311/400: 78%|███████▊ | 310/400 [00:54<00:16, 5.34it/s, v_num=1, train_loss_step=511, train_loss_epoch=511]Epoch 311/400: 78%|███████▊ | 311/400 [00:55<00:16, 5.48it/s, v_num=1, train_loss_step=511, train_loss_epoch=511]Epoch 311/400: 78%|███████▊ | 311/400 [00:55<00:16, 5.48it/s, v_num=1, train_loss_step=554, train_loss_epoch=511]Epoch 312/400: 78%|███████▊ | 311/400 [00:55<00:16, 5.48it/s, v_num=1, train_loss_step=554, train_loss_epoch=511]Epoch 312/400: 78%|███████▊ | 312/400 [00:55<00:16, 5.46it/s, v_num=1, train_loss_step=554, train_loss_epoch=511]Epoch 312/400: 78%|███████▊ | 312/400 [00:55<00:16, 5.46it/s, v_num=1, train_loss_step=518, train_loss_epoch=511]Epoch 313/400: 78%|███████▊ | 312/400 [00:55<00:16, 5.46it/s, v_num=1, train_loss_step=518, train_loss_epoch=511]Epoch 313/400: 78%|███████▊ | 313/400 [00:55<00:15, 5.64it/s, v_num=1, train_loss_step=518, train_loss_epoch=511]Epoch 313/400: 78%|███████▊ | 313/400 [00:55<00:15, 5.64it/s, v_num=1, train_loss_step=539, train_loss_epoch=511]Epoch 314/400: 78%|███████▊ | 313/400 [00:55<00:15, 5.64it/s, v_num=1, train_loss_step=539, train_loss_epoch=511]Epoch 314/400: 78%|███████▊ | 314/400 [00:55<00:15, 5.63it/s, v_num=1, train_loss_step=539, train_loss_epoch=511]Epoch 314/400: 78%|███████▊ | 314/400 [00:55<00:15, 5.63it/s, v_num=1, train_loss_step=532, train_loss_epoch=511]Epoch 315/400: 78%|███████▊ | 314/400 [00:55<00:15, 5.63it/s, v_num=1, train_loss_step=532, train_loss_epoch=511]Epoch 315/400: 79%|███████▉ | 315/400 [00:55<00:14, 5.75it/s, v_num=1, train_loss_step=532, train_loss_epoch=511]Epoch 315/400: 79%|███████▉ | 315/400 [00:55<00:14, 5.75it/s, v_num=1, train_loss_step=529, train_loss_epoch=511]Epoch 316/400: 79%|███████▉ | 315/400 [00:55<00:14, 5.75it/s, v_num=1, train_loss_step=529, train_loss_epoch=511]Epoch 316/400: 79%|███████▉ | 316/400 [00:55<00:14, 5.60it/s, v_num=1, train_loss_step=529, train_loss_epoch=511]Epoch 316/400: 79%|███████▉ | 316/400 [00:55<00:14, 5.60it/s, v_num=1, train_loss_step=516, train_loss_epoch=510]Epoch 317/400: 79%|███████▉ | 316/400 [00:56<00:14, 5.60it/s, v_num=1, train_loss_step=516, train_loss_epoch=510]Epoch 317/400: 79%|███████▉ | 317/400 [00:56<00:14, 5.56it/s, v_num=1, train_loss_step=516, train_loss_epoch=510]Epoch 317/400: 79%|███████▉ | 317/400 [00:56<00:14, 5.56it/s, v_num=1, train_loss_step=522, train_loss_epoch=511]Epoch 318/400: 79%|███████▉ | 317/400 [00:56<00:14, 5.56it/s, v_num=1, train_loss_step=522, train_loss_epoch=511]Epoch 318/400: 80%|███████▉ | 318/400 [00:56<00:14, 5.47it/s, v_num=1, train_loss_step=522, train_loss_epoch=511]Epoch 318/400: 80%|███████▉ | 318/400 [00:56<00:14, 5.47it/s, v_num=1, train_loss_step=523, train_loss_epoch=511]Epoch 319/400: 80%|███████▉ | 318/400 [00:56<00:14, 5.47it/s, v_num=1, train_loss_step=523, train_loss_epoch=511]Epoch 319/400: 80%|███████▉ | 319/400 [00:56<00:14, 5.52it/s, v_num=1, train_loss_step=523, train_loss_epoch=511]Epoch 319/400: 80%|███████▉ | 319/400 [00:56<00:14, 5.52it/s, v_num=1, train_loss_step=516, train_loss_epoch=510]Epoch 320/400: 80%|███████▉ | 319/400 [00:56<00:14, 5.52it/s, v_num=1, train_loss_step=516, train_loss_epoch=510]Epoch 320/400: 80%|████████ | 320/400 [00:56<00:13, 5.72it/s, v_num=1, train_loss_step=516, train_loss_epoch=510]Epoch 320/400: 80%|████████ | 320/400 [00:56<00:13, 5.72it/s, v_num=1, train_loss_step=526, train_loss_epoch=511]Epoch 321/400: 80%|████████ | 320/400 [00:56<00:13, 5.72it/s, v_num=1, train_loss_step=526, train_loss_epoch=511]Epoch 321/400: 80%|████████ | 321/400 [00:56<00:13, 5.91it/s, v_num=1, train_loss_step=526, train_loss_epoch=511]Epoch 321/400: 80%|████████ | 321/400 [00:56<00:13, 5.91it/s, v_num=1, train_loss_step=500, train_loss_epoch=510]Epoch 322/400: 80%|████████ | 321/400 [00:56<00:13, 5.91it/s, v_num=1, train_loss_step=500, train_loss_epoch=510]Epoch 322/400: 80%|████████ | 322/400 [00:57<00:13, 5.78it/s, v_num=1, train_loss_step=500, train_loss_epoch=510]Epoch 322/400: 80%|████████ | 322/400 [00:57<00:13, 5.78it/s, v_num=1, train_loss_step=502, train_loss_epoch=511]Epoch 323/400: 80%|████████ | 322/400 [00:57<00:13, 5.78it/s, v_num=1, train_loss_step=502, train_loss_epoch=511]Epoch 323/400: 81%|████████ | 323/400 [00:57<00:13, 5.91it/s, v_num=1, train_loss_step=502, train_loss_epoch=511]Epoch 323/400: 81%|████████ | 323/400 [00:57<00:13, 5.91it/s, v_num=1, train_loss_step=508, train_loss_epoch=511]Epoch 324/400: 81%|████████ | 323/400 [00:57<00:13, 5.91it/s, v_num=1, train_loss_step=508, train_loss_epoch=511]Epoch 324/400: 81%|████████ | 324/400 [00:57<00:12, 6.00it/s, v_num=1, train_loss_step=508, train_loss_epoch=511]Epoch 324/400: 81%|████████ | 324/400 [00:57<00:12, 6.00it/s, v_num=1, train_loss_step=493, train_loss_epoch=511]Epoch 325/400: 81%|████████ | 324/400 [00:57<00:12, 6.00it/s, v_num=1, train_loss_step=493, train_loss_epoch=511]Epoch 325/400: 81%|████████▏ | 325/400 [00:57<00:12, 6.03it/s, v_num=1, train_loss_step=493, train_loss_epoch=511]Epoch 325/400: 81%|████████▏ | 325/400 [00:57<00:12, 6.03it/s, v_num=1, train_loss_step=495, train_loss_epoch=510]Epoch 326/400: 81%|████████▏ | 325/400 [00:57<00:12, 6.03it/s, v_num=1, train_loss_step=495, train_loss_epoch=510]Epoch 326/400: 82%|████████▏ | 326/400 [00:57<00:12, 6.09it/s, v_num=1, train_loss_step=495, train_loss_epoch=510]Epoch 326/400: 82%|████████▏ | 326/400 [00:57<00:12, 6.09it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 327/400: 82%|████████▏ | 326/400 [00:57<00:12, 6.09it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 327/400: 82%|████████▏ | 327/400 [00:57<00:12, 6.07it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 327/400: 82%|████████▏ | 327/400 [00:57<00:12, 6.07it/s, v_num=1, train_loss_step=513, train_loss_epoch=510]Epoch 328/400: 82%|████████▏ | 327/400 [00:57<00:12, 6.07it/s, v_num=1, train_loss_step=513, train_loss_epoch=510]Epoch 328/400: 82%|████████▏ | 328/400 [00:58<00:12, 5.95it/s, v_num=1, train_loss_step=513, train_loss_epoch=510]Epoch 328/400: 82%|████████▏ | 328/400 [00:58<00:12, 5.95it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 329/400: 82%|████████▏ | 328/400 [00:58<00:12, 5.95it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 329/400: 82%|████████▏ | 329/400 [00:58<00:12, 5.87it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 329/400: 82%|████████▏ | 329/400 [00:58<00:12, 5.87it/s, v_num=1, train_loss_step=517, train_loss_epoch=510]Epoch 330/400: 82%|████████▏ | 329/400 [00:58<00:12, 5.87it/s, v_num=1, train_loss_step=517, train_loss_epoch=510]Epoch 330/400: 82%|████████▎ | 330/400 [00:58<00:11, 5.91it/s, v_num=1, train_loss_step=517, train_loss_epoch=510]Epoch 330/400: 82%|████████▎ | 330/400 [00:58<00:11, 5.91it/s, v_num=1, train_loss_step=498, train_loss_epoch=510]Epoch 331/400: 82%|████████▎ | 330/400 [00:58<00:11, 5.91it/s, v_num=1, train_loss_step=498, train_loss_epoch=510]Epoch 331/400: 83%|████████▎ | 331/400 [00:58<00:11, 5.84it/s, v_num=1, train_loss_step=498, train_loss_epoch=510]Epoch 331/400: 83%|████████▎ | 331/400 [00:58<00:11, 5.84it/s, v_num=1, train_loss_step=490, train_loss_epoch=511]Epoch 332/400: 83%|████████▎ | 331/400 [00:58<00:11, 5.84it/s, v_num=1, train_loss_step=490, train_loss_epoch=511]Epoch 332/400: 83%|████████▎ | 332/400 [00:58<00:11, 5.87it/s, v_num=1, train_loss_step=490, train_loss_epoch=511]Epoch 332/400: 83%|████████▎ | 332/400 [00:58<00:11, 5.87it/s, v_num=1, train_loss_step=493, train_loss_epoch=510]Epoch 333/400: 83%|████████▎ | 332/400 [00:58<00:11, 5.87it/s, v_num=1, train_loss_step=493, train_loss_epoch=510]Epoch 333/400: 83%|████████▎ | 333/400 [00:58<00:11, 5.63it/s, v_num=1, train_loss_step=493, train_loss_epoch=510]Epoch 333/400: 83%|████████▎ | 333/400 [00:58<00:11, 5.63it/s, v_num=1, train_loss_step=504, train_loss_epoch=510]Epoch 334/400: 83%|████████▎ | 333/400 [00:58<00:11, 5.63it/s, v_num=1, train_loss_step=504, train_loss_epoch=510]Epoch 334/400: 84%|████████▎ | 334/400 [00:59<00:12, 5.46it/s, v_num=1, train_loss_step=504, train_loss_epoch=510]Epoch 334/400: 84%|████████▎ | 334/400 [00:59<00:12, 5.46it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 335/400: 84%|████████▎ | 334/400 [00:59<00:12, 5.46it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 335/400: 84%|████████▍ | 335/400 [00:59<00:11, 5.45it/s, v_num=1, train_loss_step=506, train_loss_epoch=510]Epoch 335/400: 84%|████████▍ | 335/400 [00:59<00:11, 5.45it/s, v_num=1, train_loss_step=510, train_loss_epoch=510]Epoch 336/400: 84%|████████▍ | 335/400 [00:59<00:11, 5.45it/s, v_num=1, train_loss_step=510, train_loss_epoch=510]Epoch 336/400: 84%|████████▍ | 336/400 [00:59<00:11, 5.69it/s, v_num=1, train_loss_step=510, train_loss_epoch=510]Epoch 336/400: 84%|████████▍ | 336/400 [00:59<00:11, 5.69it/s, v_num=1, train_loss_step=523, train_loss_epoch=509]Epoch 337/400: 84%|████████▍ | 336/400 [00:59<00:11, 5.69it/s, v_num=1, train_loss_step=523, train_loss_epoch=509]Epoch 337/400: 84%|████████▍ | 337/400 [00:59<00:11, 5.72it/s, v_num=1, train_loss_step=523, train_loss_epoch=509]Epoch 337/400: 84%|████████▍ | 337/400 [00:59<00:11, 5.72it/s, v_num=1, train_loss_step=525, train_loss_epoch=510]Epoch 338/400: 84%|████████▍ | 337/400 [00:59<00:11, 5.72it/s, v_num=1, train_loss_step=525, train_loss_epoch=510]Epoch 338/400: 84%|████████▍ | 338/400 [00:59<00:10, 5.83it/s, v_num=1, train_loss_step=525, train_loss_epoch=510]Epoch 338/400: 84%|████████▍ | 338/400 [00:59<00:10, 5.83it/s, v_num=1, train_loss_step=517, train_loss_epoch=510]Epoch 339/400: 84%|████████▍ | 338/400 [00:59<00:10, 5.83it/s, v_num=1, train_loss_step=517, train_loss_epoch=510]Epoch 339/400: 85%|████████▍ | 339/400 [00:59<00:10, 5.73it/s, v_num=1, train_loss_step=517, train_loss_epoch=510]Epoch 339/400: 85%|████████▍ | 339/400 [00:59<00:10, 5.73it/s, v_num=1, train_loss_step=536, train_loss_epoch=510]Epoch 340/400: 85%|████████▍ | 339/400 [00:59<00:10, 5.73it/s, v_num=1, train_loss_step=536, train_loss_epoch=510]Epoch 340/400: 85%|████████▌ | 340/400 [01:00<00:10, 5.83it/s, v_num=1, train_loss_step=536, train_loss_epoch=510]Epoch 340/400: 85%|████████▌ | 340/400 [01:00<00:10, 5.83it/s, v_num=1, train_loss_step=496, train_loss_epoch=509]Epoch 341/400: 85%|████████▌ | 340/400 [01:00<00:10, 5.83it/s, v_num=1, train_loss_step=496, train_loss_epoch=509]Epoch 341/400: 85%|████████▌ | 341/400 [01:00<00:10, 5.66it/s, v_num=1, train_loss_step=496, train_loss_epoch=509]Epoch 341/400: 85%|████████▌ | 341/400 [01:00<00:10, 5.66it/s, v_num=1, train_loss_step=501, train_loss_epoch=510]Epoch 342/400: 85%|████████▌ | 341/400 [01:00<00:10, 5.66it/s, v_num=1, train_loss_step=501, train_loss_epoch=510]Epoch 342/400: 86%|████████▌ | 342/400 [01:00<00:10, 5.36it/s, v_num=1, train_loss_step=501, train_loss_epoch=510]Epoch 342/400: 86%|████████▌ | 342/400 [01:00<00:10, 5.36it/s, v_num=1, train_loss_step=515, train_loss_epoch=509]Epoch 343/400: 86%|████████▌ | 342/400 [01:00<00:10, 5.36it/s, v_num=1, train_loss_step=515, train_loss_epoch=509]Epoch 343/400: 86%|████████▌ | 343/400 [01:00<00:10, 5.26it/s, v_num=1, train_loss_step=515, train_loss_epoch=509]Epoch 343/400: 86%|████████▌ | 343/400 [01:00<00:10, 5.26it/s, v_num=1, train_loss_step=541, train_loss_epoch=510]Epoch 344/400: 86%|████████▌ | 343/400 [01:00<00:10, 5.26it/s, v_num=1, train_loss_step=541, train_loss_epoch=510]Epoch 344/400: 86%|████████▌ | 344/400 [01:00<00:10, 5.31it/s, v_num=1, train_loss_step=541, train_loss_epoch=510]Epoch 344/400: 86%|████████▌ | 344/400 [01:00<00:10, 5.31it/s, v_num=1, train_loss_step=498, train_loss_epoch=510]Epoch 345/400: 86%|████████▌ | 344/400 [01:00<00:10, 5.31it/s, v_num=1, train_loss_step=498, train_loss_epoch=510]Epoch 345/400: 86%|████████▋ | 345/400 [01:01<00:10, 5.25it/s, v_num=1, train_loss_step=498, train_loss_epoch=510]Epoch 345/400: 86%|████████▋ | 345/400 [01:01<00:10, 5.25it/s, v_num=1, train_loss_step=493, train_loss_epoch=509]Epoch 346/400: 86%|████████▋ | 345/400 [01:01<00:10, 5.25it/s, v_num=1, train_loss_step=493, train_loss_epoch=509]Epoch 346/400: 86%|████████▋ | 346/400 [01:01<00:10, 5.16it/s, v_num=1, train_loss_step=493, train_loss_epoch=509]Epoch 346/400: 86%|████████▋ | 346/400 [01:01<00:10, 5.16it/s, v_num=1, train_loss_step=488, train_loss_epoch=509]Epoch 347/400: 86%|████████▋ | 346/400 [01:01<00:10, 5.16it/s, v_num=1, train_loss_step=488, train_loss_epoch=509]Epoch 347/400: 87%|████████▋ | 347/400 [01:01<00:10, 5.06it/s, v_num=1, train_loss_step=488, train_loss_epoch=509]Epoch 347/400: 87%|████████▋ | 347/400 [01:01<00:10, 5.06it/s, v_num=1, train_loss_step=492, train_loss_epoch=510]Epoch 348/400: 87%|████████▋ | 347/400 [01:01<00:10, 5.06it/s, v_num=1, train_loss_step=492, train_loss_epoch=510]Epoch 348/400: 87%|████████▋ | 348/400 [01:01<00:10, 5.03it/s, v_num=1, train_loss_step=492, train_loss_epoch=510]Epoch 348/400: 87%|████████▋ | 348/400 [01:01<00:10, 5.03it/s, v_num=1, train_loss_step=517, train_loss_epoch=509]Epoch 349/400: 87%|████████▋ | 348/400 [01:01<00:10, 5.03it/s, v_num=1, train_loss_step=517, train_loss_epoch=509]Epoch 349/400: 87%|████████▋ | 349/400 [01:01<00:09, 5.37it/s, v_num=1, train_loss_step=517, train_loss_epoch=509]Epoch 349/400: 87%|████████▋ | 349/400 [01:01<00:09, 5.37it/s, v_num=1, train_loss_step=516, train_loss_epoch=509]Epoch 350/400: 87%|████████▋ | 349/400 [01:01<00:09, 5.37it/s, v_num=1, train_loss_step=516, train_loss_epoch=509]Epoch 350/400: 88%|████████▊ | 350/400 [01:02<00:09, 5.41it/s, v_num=1, train_loss_step=516, train_loss_epoch=509]Epoch 350/400: 88%|████████▊ | 350/400 [01:02<00:09, 5.41it/s, v_num=1, train_loss_step=496, train_loss_epoch=509]Epoch 351/400: 88%|████████▊ | 350/400 [01:02<00:09, 5.41it/s, v_num=1, train_loss_step=496, train_loss_epoch=509]Epoch 351/400: 88%|████████▊ | 351/400 [01:02<00:09, 5.34it/s, v_num=1, train_loss_step=496, train_loss_epoch=509]Epoch 351/400: 88%|████████▊ | 351/400 [01:02<00:09, 5.34it/s, v_num=1, train_loss_step=559, train_loss_epoch=510]Epoch 352/400: 88%|████████▊ | 351/400 [01:02<00:09, 5.34it/s, v_num=1, train_loss_step=559, train_loss_epoch=510]Epoch 352/400: 88%|████████▊ | 352/400 [01:02<00:08, 5.44it/s, v_num=1, train_loss_step=559, train_loss_epoch=510]Epoch 352/400: 88%|████████▊ | 352/400 [01:02<00:08, 5.44it/s, v_num=1, train_loss_step=468, train_loss_epoch=509]Epoch 353/400: 88%|████████▊ | 352/400 [01:02<00:08, 5.44it/s, v_num=1, train_loss_step=468, train_loss_epoch=509]Epoch 353/400: 88%|████████▊ | 353/400 [01:02<00:08, 5.49it/s, v_num=1, train_loss_step=468, train_loss_epoch=509]Epoch 353/400: 88%|████████▊ | 353/400 [01:02<00:08, 5.49it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 354/400: 88%|████████▊ | 353/400 [01:02<00:08, 5.49it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 354/400: 88%|████████▊ | 354/400 [01:02<00:08, 5.62it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 354/400: 88%|████████▊ | 354/400 [01:02<00:08, 5.62it/s, v_num=1, train_loss_step=490, train_loss_epoch=510]Epoch 355/400: 88%|████████▊ | 354/400 [01:02<00:08, 5.62it/s, v_num=1, train_loss_step=490, train_loss_epoch=510]Epoch 355/400: 89%|████████▉ | 355/400 [01:02<00:07, 5.70it/s, v_num=1, train_loss_step=490, train_loss_epoch=510]Epoch 355/400: 89%|████████▉ | 355/400 [01:02<00:07, 5.70it/s, v_num=1, train_loss_step=500, train_loss_epoch=510]Epoch 356/400: 89%|████████▉ | 355/400 [01:02<00:07, 5.70it/s, v_num=1, train_loss_step=500, train_loss_epoch=510]Epoch 356/400: 89%|████████▉ | 356/400 [01:03<00:07, 5.56it/s, v_num=1, train_loss_step=500, train_loss_epoch=510]Epoch 356/400: 89%|████████▉ | 356/400 [01:03<00:07, 5.56it/s, v_num=1, train_loss_step=505, train_loss_epoch=510]Epoch 357/400: 89%|████████▉ | 356/400 [01:03<00:07, 5.56it/s, v_num=1, train_loss_step=505, train_loss_epoch=510]Epoch 357/400: 89%|████████▉ | 357/400 [01:03<00:07, 5.66it/s, v_num=1, train_loss_step=505, train_loss_epoch=510]Epoch 357/400: 89%|████████▉ | 357/400 [01:03<00:07, 5.66it/s, v_num=1, train_loss_step=509, train_loss_epoch=508]Epoch 358/400: 89%|████████▉ | 357/400 [01:03<00:07, 5.66it/s, v_num=1, train_loss_step=509, train_loss_epoch=508]Epoch 358/400: 90%|████████▉ | 358/400 [01:03<00:07, 5.71it/s, v_num=1, train_loss_step=509, train_loss_epoch=508]Epoch 358/400: 90%|████████▉ | 358/400 [01:03<00:07, 5.71it/s, v_num=1, train_loss_step=524, train_loss_epoch=509]Epoch 359/400: 90%|████████▉ | 358/400 [01:03<00:07, 5.71it/s, v_num=1, train_loss_step=524, train_loss_epoch=509]Epoch 359/400: 90%|████████▉ | 359/400 [01:03<00:07, 5.70it/s, v_num=1, train_loss_step=524, train_loss_epoch=509]Epoch 359/400: 90%|████████▉ | 359/400 [01:03<00:07, 5.70it/s, v_num=1, train_loss_step=513, train_loss_epoch=509]Epoch 360/400: 90%|████████▉ | 359/400 [01:03<00:07, 5.70it/s, v_num=1, train_loss_step=513, train_loss_epoch=509]Epoch 360/400: 90%|█████████ | 360/400 [01:03<00:06, 5.78it/s, v_num=1, train_loss_step=513, train_loss_epoch=509]Epoch 360/400: 90%|█████████ | 360/400 [01:03<00:06, 5.78it/s, v_num=1, train_loss_step=483, train_loss_epoch=509]Epoch 361/400: 90%|█████████ | 360/400 [01:03<00:06, 5.78it/s, v_num=1, train_loss_step=483, train_loss_epoch=509]Epoch 361/400: 90%|█████████ | 361/400 [01:03<00:06, 5.80it/s, v_num=1, train_loss_step=483, train_loss_epoch=509]Epoch 361/400: 90%|█████████ | 361/400 [01:03<00:06, 5.80it/s, v_num=1, train_loss_step=519, train_loss_epoch=508]Epoch 362/400: 90%|█████████ | 361/400 [01:03<00:06, 5.80it/s, v_num=1, train_loss_step=519, train_loss_epoch=508]Epoch 362/400: 90%|█████████ | 362/400 [01:04<00:06, 5.86it/s, v_num=1, train_loss_step=519, train_loss_epoch=508]Epoch 362/400: 90%|█████████ | 362/400 [01:04<00:06, 5.86it/s, v_num=1, train_loss_step=531, train_loss_epoch=509]Epoch 363/400: 90%|█████████ | 362/400 [01:04<00:06, 5.86it/s, v_num=1, train_loss_step=531, train_loss_epoch=509]Epoch 363/400: 91%|█████████ | 363/400 [01:04<00:06, 5.92it/s, v_num=1, train_loss_step=531, train_loss_epoch=509]Epoch 363/400: 91%|█████████ | 363/400 [01:04<00:06, 5.92it/s, v_num=1, train_loss_step=506, train_loss_epoch=509]Epoch 364/400: 91%|█████████ | 363/400 [01:04<00:06, 5.92it/s, v_num=1, train_loss_step=506, train_loss_epoch=509]Epoch 364/400: 91%|█████████ | 364/400 [01:04<00:06, 5.95it/s, v_num=1, train_loss_step=506, train_loss_epoch=509]Epoch 364/400: 91%|█████████ | 364/400 [01:04<00:06, 5.95it/s, v_num=1, train_loss_step=492, train_loss_epoch=508]Epoch 365/400: 91%|█████████ | 364/400 [01:04<00:06, 5.95it/s, v_num=1, train_loss_step=492, train_loss_epoch=508]Epoch 365/400: 91%|█████████▏| 365/400 [01:04<00:06, 5.65it/s, v_num=1, train_loss_step=492, train_loss_epoch=508]Epoch 365/400: 91%|█████████▏| 365/400 [01:04<00:06, 5.65it/s, v_num=1, train_loss_step=494, train_loss_epoch=508]Epoch 366/400: 91%|█████████▏| 365/400 [01:04<00:06, 5.65it/s, v_num=1, train_loss_step=494, train_loss_epoch=508]Epoch 366/400: 92%|█████████▏| 366/400 [01:04<00:06, 5.63it/s, v_num=1, train_loss_step=494, train_loss_epoch=508]Epoch 366/400: 92%|█████████▏| 366/400 [01:04<00:06, 5.63it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 367/400: 92%|█████████▏| 366/400 [01:04<00:06, 5.63it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 367/400: 92%|█████████▏| 367/400 [01:05<00:05, 5.58it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 367/400: 92%|█████████▏| 367/400 [01:05<00:05, 5.58it/s, v_num=1, train_loss_step=520, train_loss_epoch=509]Epoch 368/400: 92%|█████████▏| 367/400 [01:05<00:05, 5.58it/s, v_num=1, train_loss_step=520, train_loss_epoch=509]Epoch 368/400: 92%|█████████▏| 368/400 [01:05<00:05, 5.62it/s, v_num=1, train_loss_step=520, train_loss_epoch=509]Epoch 368/400: 92%|█████████▏| 368/400 [01:05<00:05, 5.62it/s, v_num=1, train_loss_step=509, train_loss_epoch=509]Epoch 369/400: 92%|█████████▏| 368/400 [01:05<00:05, 5.62it/s, v_num=1, train_loss_step=509, train_loss_epoch=509]Epoch 369/400: 92%|█████████▏| 369/400 [01:05<00:05, 5.49it/s, v_num=1, train_loss_step=509, train_loss_epoch=509]Epoch 369/400: 92%|█████████▏| 369/400 [01:05<00:05, 5.49it/s, v_num=1, train_loss_step=512, train_loss_epoch=509]Epoch 370/400: 92%|█████████▏| 369/400 [01:05<00:05, 5.49it/s, v_num=1, train_loss_step=512, train_loss_epoch=509]Epoch 370/400: 92%|█████████▎| 370/400 [01:05<00:05, 5.19it/s, v_num=1, train_loss_step=512, train_loss_epoch=509]Epoch 370/400: 92%|█████████▎| 370/400 [01:05<00:05, 5.19it/s, v_num=1, train_loss_step=508, train_loss_epoch=508]Epoch 371/400: 92%|█████████▎| 370/400 [01:05<00:05, 5.19it/s, v_num=1, train_loss_step=508, train_loss_epoch=508]Epoch 371/400: 93%|█████████▎| 371/400 [01:05<00:05, 5.08it/s, v_num=1, train_loss_step=508, train_loss_epoch=508]Epoch 371/400: 93%|█████████▎| 371/400 [01:05<00:05, 5.08it/s, v_num=1, train_loss_step=499, train_loss_epoch=508]Epoch 372/400: 93%|█████████▎| 371/400 [01:05<00:05, 5.08it/s, v_num=1, train_loss_step=499, train_loss_epoch=508]Epoch 372/400: 93%|█████████▎| 372/400 [01:06<00:05, 5.12it/s, v_num=1, train_loss_step=499, train_loss_epoch=508]Epoch 372/400: 93%|█████████▎| 372/400 [01:06<00:05, 5.12it/s, v_num=1, train_loss_step=541, train_loss_epoch=508]Epoch 373/400: 93%|█████████▎| 372/400 [01:06<00:05, 5.12it/s, v_num=1, train_loss_step=541, train_loss_epoch=508]Epoch 373/400: 93%|█████████▎| 373/400 [01:06<00:05, 5.19it/s, v_num=1, train_loss_step=541, train_loss_epoch=508]Epoch 373/400: 93%|█████████▎| 373/400 [01:06<00:05, 5.19it/s, v_num=1, train_loss_step=501, train_loss_epoch=508]Epoch 374/400: 93%|█████████▎| 373/400 [01:06<00:05, 5.19it/s, v_num=1, train_loss_step=501, train_loss_epoch=508]Epoch 374/400: 94%|█████████▎| 374/400 [01:06<00:04, 5.38it/s, v_num=1, train_loss_step=501, train_loss_epoch=508]Epoch 374/400: 94%|█████████▎| 374/400 [01:06<00:04, 5.38it/s, v_num=1, train_loss_step=497, train_loss_epoch=508]Epoch 375/400: 94%|█████████▎| 374/400 [01:06<00:04, 5.38it/s, v_num=1, train_loss_step=497, train_loss_epoch=508]Epoch 375/400: 94%|█████████▍| 375/400 [01:06<00:04, 5.56it/s, v_num=1, train_loss_step=497, train_loss_epoch=508]Epoch 375/400: 94%|█████████▍| 375/400 [01:06<00:04, 5.56it/s, v_num=1, train_loss_step=509, train_loss_epoch=508]Epoch 376/400: 94%|█████████▍| 375/400 [01:06<00:04, 5.56it/s, v_num=1, train_loss_step=509, train_loss_epoch=508]Epoch 376/400: 94%|█████████▍| 376/400 [01:06<00:04, 5.71it/s, v_num=1, train_loss_step=509, train_loss_epoch=508]Epoch 376/400: 94%|█████████▍| 376/400 [01:06<00:04, 5.71it/s, v_num=1, train_loss_step=512, train_loss_epoch=509]Epoch 377/400: 94%|█████████▍| 376/400 [01:06<00:04, 5.71it/s, v_num=1, train_loss_step=512, train_loss_epoch=509]Epoch 377/400: 94%|█████████▍| 377/400 [01:06<00:04, 5.52it/s, v_num=1, train_loss_step=512, train_loss_epoch=509]Epoch 377/400: 94%|█████████▍| 377/400 [01:06<00:04, 5.52it/s, v_num=1, train_loss_step=497, train_loss_epoch=508]Epoch 378/400: 94%|█████████▍| 377/400 [01:06<00:04, 5.52it/s, v_num=1, train_loss_step=497, train_loss_epoch=508]Epoch 378/400: 94%|█████████▍| 378/400 [01:07<00:04, 5.32it/s, v_num=1, train_loss_step=497, train_loss_epoch=508]Epoch 378/400: 94%|█████████▍| 378/400 [01:07<00:04, 5.32it/s, v_num=1, train_loss_step=515, train_loss_epoch=508]Epoch 379/400: 94%|█████████▍| 378/400 [01:07<00:04, 5.32it/s, v_num=1, train_loss_step=515, train_loss_epoch=508]Epoch 379/400: 95%|█████████▍| 379/400 [01:07<00:04, 5.18it/s, v_num=1, train_loss_step=515, train_loss_epoch=508]Epoch 379/400: 95%|█████████▍| 379/400 [01:07<00:04, 5.18it/s, v_num=1, train_loss_step=513, train_loss_epoch=508]Epoch 380/400: 95%|█████████▍| 379/400 [01:07<00:04, 5.18it/s, v_num=1, train_loss_step=513, train_loss_epoch=508]Epoch 380/400: 95%|█████████▌| 380/400 [01:07<00:03, 5.10it/s, v_num=1, train_loss_step=513, train_loss_epoch=508]Epoch 380/400: 95%|█████████▌| 380/400 [01:07<00:03, 5.10it/s, v_num=1, train_loss_step=495, train_loss_epoch=508]Epoch 381/400: 95%|█████████▌| 380/400 [01:07<00:03, 5.10it/s, v_num=1, train_loss_step=495, train_loss_epoch=508]Epoch 381/400: 95%|█████████▌| 381/400 [01:07<00:03, 5.15it/s, v_num=1, train_loss_step=495, train_loss_epoch=508]Epoch 381/400: 95%|█████████▌| 381/400 [01:07<00:03, 5.15it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 382/400: 95%|█████████▌| 381/400 [01:07<00:03, 5.15it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 382/400: 96%|█████████▌| 382/400 [01:07<00:03, 5.27it/s, v_num=1, train_loss_step=501, train_loss_epoch=509]Epoch 382/400: 96%|█████████▌| 382/400 [01:07<00:03, 5.27it/s, v_num=1, train_loss_step=533, train_loss_epoch=508]Epoch 383/400: 96%|█████████▌| 382/400 [01:07<00:03, 5.27it/s, v_num=1, train_loss_step=533, train_loss_epoch=508]Epoch 383/400: 96%|█████████▌| 383/400 [01:08<00:03, 5.47it/s, v_num=1, train_loss_step=533, train_loss_epoch=508]Epoch 383/400: 96%|█████████▌| 383/400 [01:08<00:03, 5.47it/s, v_num=1, train_loss_step=490, train_loss_epoch=508]Epoch 384/400: 96%|█████████▌| 383/400 [01:08<00:03, 5.47it/s, v_num=1, train_loss_step=490, train_loss_epoch=508]Epoch 384/400: 96%|█████████▌| 384/400 [01:08<00:02, 5.58it/s, v_num=1, train_loss_step=490, train_loss_epoch=508]Epoch 384/400: 96%|█████████▌| 384/400 [01:08<00:02, 5.58it/s, v_num=1, train_loss_step=530, train_loss_epoch=508]Epoch 385/400: 96%|█████████▌| 384/400 [01:08<00:02, 5.58it/s, v_num=1, train_loss_step=530, train_loss_epoch=508]Epoch 385/400: 96%|█████████▋| 385/400 [01:08<00:02, 5.49it/s, v_num=1, train_loss_step=530, train_loss_epoch=508]Epoch 385/400: 96%|█████████▋| 385/400 [01:08<00:02, 5.49it/s, v_num=1, train_loss_step=536, train_loss_epoch=508]Epoch 386/400: 96%|█████████▋| 385/400 [01:08<00:02, 5.49it/s, v_num=1, train_loss_step=536, train_loss_epoch=508]Epoch 386/400: 96%|█████████▋| 386/400 [01:08<00:02, 5.34it/s, v_num=1, train_loss_step=536, train_loss_epoch=508]Epoch 386/400: 96%|█████████▋| 386/400 [01:08<00:02, 5.34it/s, v_num=1, train_loss_step=531, train_loss_epoch=508]Epoch 387/400: 96%|█████████▋| 386/400 [01:08<00:02, 5.34it/s, v_num=1, train_loss_step=531, train_loss_epoch=508]Epoch 387/400: 97%|█████████▋| 387/400 [01:08<00:02, 5.25it/s, v_num=1, train_loss_step=531, train_loss_epoch=508]Epoch 387/400: 97%|█████████▋| 387/400 [01:08<00:02, 5.25it/s, v_num=1, train_loss_step=541, train_loss_epoch=508]Epoch 388/400: 97%|█████████▋| 387/400 [01:08<00:02, 5.25it/s, v_num=1, train_loss_step=541, train_loss_epoch=508]Epoch 388/400: 97%|█████████▋| 388/400 [01:09<00:02, 5.26it/s, v_num=1, train_loss_step=541, train_loss_epoch=508]Epoch 388/400: 97%|█████████▋| 388/400 [01:09<00:02, 5.26it/s, v_num=1, train_loss_step=531, train_loss_epoch=508]Epoch 389/400: 97%|█████████▋| 388/400 [01:09<00:02, 5.26it/s, v_num=1, train_loss_step=531, train_loss_epoch=508]Epoch 389/400: 97%|█████████▋| 389/400 [01:09<00:02, 5.12it/s, v_num=1, train_loss_step=531, train_loss_epoch=508]Epoch 389/400: 97%|█████████▋| 389/400 [01:09<00:02, 5.12it/s, v_num=1, train_loss_step=514, train_loss_epoch=508]Epoch 390/400: 97%|█████████▋| 389/400 [01:09<00:02, 5.12it/s, v_num=1, train_loss_step=514, train_loss_epoch=508]Epoch 390/400: 98%|█████████▊| 390/400 [01:09<00:01, 5.24it/s, v_num=1, train_loss_step=514, train_loss_epoch=508]Epoch 390/400: 98%|█████████▊| 390/400 [01:09<00:01, 5.24it/s, v_num=1, train_loss_step=499, train_loss_epoch=508]Epoch 391/400: 98%|█████████▊| 390/400 [01:09<00:01, 5.24it/s, v_num=1, train_loss_step=499, train_loss_epoch=508]Epoch 391/400: 98%|█████████▊| 391/400 [01:09<00:01, 5.42it/s, v_num=1, train_loss_step=499, train_loss_epoch=508]Epoch 391/400: 98%|█████████▊| 391/400 [01:09<00:01, 5.42it/s, v_num=1, train_loss_step=498, train_loss_epoch=508]Epoch 392/400: 98%|█████████▊| 391/400 [01:09<00:01, 5.42it/s, v_num=1, train_loss_step=498, train_loss_epoch=508]Epoch 392/400: 98%|█████████▊| 392/400 [01:09<00:01, 5.54it/s, v_num=1, train_loss_step=498, train_loss_epoch=508]Epoch 392/400: 98%|█████████▊| 392/400 [01:09<00:01, 5.54it/s, v_num=1, train_loss_step=510, train_loss_epoch=508]Epoch 393/400: 98%|█████████▊| 392/400 [01:09<00:01, 5.54it/s, v_num=1, train_loss_step=510, train_loss_epoch=508]Epoch 393/400: 98%|█████████▊| 393/400 [01:09<00:01, 5.44it/s, v_num=1, train_loss_step=510, train_loss_epoch=508]Epoch 393/400: 98%|█████████▊| 393/400 [01:09<00:01, 5.44it/s, v_num=1, train_loss_step=505, train_loss_epoch=508]Epoch 394/400: 98%|█████████▊| 393/400 [01:09<00:01, 5.44it/s, v_num=1, train_loss_step=505, train_loss_epoch=508]Epoch 394/400: 98%|█████████▊| 394/400 [01:10<00:01, 5.31it/s, v_num=1, train_loss_step=505, train_loss_epoch=508]Epoch 394/400: 98%|█████████▊| 394/400 [01:10<00:01, 5.31it/s, v_num=1, train_loss_step=485, train_loss_epoch=508]Epoch 395/400: 98%|█████████▊| 394/400 [01:10<00:01, 5.31it/s, v_num=1, train_loss_step=485, train_loss_epoch=508]Epoch 395/400: 99%|█████████▉| 395/400 [01:10<00:00, 5.18it/s, v_num=1, train_loss_step=485, train_loss_epoch=508]Epoch 395/400: 99%|█████████▉| 395/400 [01:10<00:00, 5.18it/s, v_num=1, train_loss_step=517, train_loss_epoch=508]Epoch 396/400: 99%|█████████▉| 395/400 [01:10<00:00, 5.18it/s, v_num=1, train_loss_step=517, train_loss_epoch=508]Epoch 396/400: 99%|█████████▉| 396/400 [01:10<00:00, 5.18it/s, v_num=1, train_loss_step=517, train_loss_epoch=508]Epoch 396/400: 99%|█████████▉| 396/400 [01:10<00:00, 5.18it/s, v_num=1, train_loss_step=482, train_loss_epoch=508]Epoch 397/400: 99%|█████████▉| 396/400 [01:10<00:00, 5.18it/s, v_num=1, train_loss_step=482, train_loss_epoch=508]Epoch 397/400: 99%|█████████▉| 397/400 [01:10<00:00, 5.27it/s, v_num=1, train_loss_step=482, train_loss_epoch=508]Epoch 397/400: 99%|█████████▉| 397/400 [01:10<00:00, 5.27it/s, v_num=1, train_loss_step=522, train_loss_epoch=507]Epoch 398/400: 99%|█████████▉| 397/400 [01:10<00:00, 5.27it/s, v_num=1, train_loss_step=522, train_loss_epoch=507]Epoch 398/400: 100%|█████████▉| 398/400 [01:10<00:00, 5.50it/s, v_num=1, train_loss_step=522, train_loss_epoch=507]Epoch 398/400: 100%|█████████▉| 398/400 [01:10<00:00, 5.50it/s, v_num=1, train_loss_step=527, train_loss_epoch=508]Epoch 399/400: 100%|█████████▉| 398/400 [01:10<00:00, 5.50it/s, v_num=1, train_loss_step=527, train_loss_epoch=508]Epoch 399/400: 100%|█████████▉| 399/400 [01:11<00:00, 5.58it/s, v_num=1, train_loss_step=527, train_loss_epoch=508]Epoch 399/400: 100%|█████████▉| 399/400 [01:11<00:00, 5.58it/s, v_num=1, train_loss_step=536, train_loss_epoch=507]Epoch 400/400: 100%|█████████▉| 399/400 [01:11<00:00, 5.58it/s, v_num=1, train_loss_step=536, train_loss_epoch=507]Epoch 400/400: 100%|██████████| 400/400 [01:11<00:00, 5.68it/s, v_num=1, train_loss_step=536, train_loss_epoch=507]Epoch 400/400: 100%|██████████| 400/400 [01:11<00:00, 5.68it/s, v_num=1, train_loss_step=512, train_loss_epoch=508]Epoch 400/400: 100%|██████████| 400/400 [01:11<00:00, 5.62it/s, v_num=1, train_loss_step=512, train_loss_epoch=508]After the integration finished, both corrected expression matrices can be found saved in the Seurat object and can be used for cluster calculations and UMAP projections. In this case, we will continue with Seuratv5 CCA Integration method.
Seu_obj <- FindNeighbors(object = Seu_obj, reduction = "integrated.cca", dims = 1:50) #change dims accrodingly
#> Computing nearest neighbor graph
#> Computing SNN
Seu_obj <- FindClusters(Seu_obj, resolution = 0.3, algorithm=4, random.seed = 1234)
Seu_obj <- RunUMAP(object = Seu_obj, reduction = "integrated.cca", dims = 1:50)
#> 17:48:51 UMAP embedding parameters a = 0.9922 b = 1.112
#> 17:48:51 Read 2807 rows and found 50 numeric columns
#> 17:48:51 Using Annoy for neighbor search, n_neighbors = 30
#> 17:48:51 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 17:48:52 Writing NN index file to temp file /tmp/Rtmp9zSWgB/file169324120f0f72
#> 17:48:52 Searching Annoy index using 1 thread, search_k = 3000
#> 17:48:52 Annoy recall = 100%
#> 17:48:53 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
#> 17:48:54 Initializing from normalized Laplacian + noise (using RSpectra)
#> 17:48:54 Commencing optimization for 500 epochs, with 129308 positive edges
#> 17:48:54 Using rng type: pcg
#> 17:48:57 Optimization finished
DO.UMAP(Seu_obj, group.by = "seurat_clusters")
DO.UMAP(Seu_obj, group.by = "condition", legend.position = "right", label = F)
Semi-automatic annotation with Celltypist
Next up, we implemented a wrapper around the semi-automatic
annotation tool celltypist. It
will annotate the defined clusters based on the
Adult_COVID19_PBMC.pkl model.
Seu_obj <- DO.CellTypist(Seu_obj,
modelName = "Healthy_COVID19_PBMC.pkl",
runCelltypistUpdate=T,
over_clustering = "seurat_clusters")
#> 2025-06-26 17:48:58 - Running celltypist using model: Healthy_COVID19_PBMC.pkl
#> 2025-06-26 17:48:58 - Saving celltypist results to temporary folder: /tmp/Rtmp9zSWgB/file1693246c47789d
#> 2025-06-26 17:49:20 - Running Celltypist
#> 2025-06-26 17:49:24 - Creating probality plot
DO.UMAP(Seu_obj, group.by = "autoAnnot", legend.position = "right")
The semi-automatic annotation is a good estimate of the cell types in
your object. But you should always manually validate the findings of the
model. You can manually define a set of marker genes for the cell
population or check the most preeminent genes per cluster by using
Seurat’s FindAllMarkers function.
#pick top 5 per cluster
annotation_Markers <- FindAllMarkers(object = Seu_obj,
assay = "RNA",
group.by = "autoAnnot",
min.pct = 0.25,
logfc.threshold = 0.25)
#> Calculating cluster CD14_mono
#> Calculating cluster CD4.Naive
#> Calculating cluster CD8.Naive
#> Calculating cluster NK_16hi
#> Calculating cluster CD8.EM
#> Calculating cluster B_naive
#> Calculating cluster pDC
annotation_Markers <- annotation_Markers %>%
arrange(desc(avg_log2FC)) %>%
distinct(gene, .keep_all = TRUE) %>%
group_by(cluster) %>%
slice_head(n = 5)
p1 <- DO.Dotplot(Seu_object = Seu_obj,
Feature = annotation_Markers,
group.by.x = "seurat_clusters",
plot.margin = c(1,1,1,1),
annotation_x = T,
point_stroke = 0.1,
annotation_x_rev = T,
textSize = 14,
hjust = 0.5,
vjust = 0,
textRot = 0,
segWidth = 0.3,
lwd = 3)
#> Scale for size is already present.
#> Adding another scale for size, which will replace the existing scale.
#manual set of markers
annotation_Markers <- data.frame(cluster = c("ImmuneCells",
rep("B_cells", 3),
rep("T_cells",3),
rep("NK", 2),
rep("Myeloid",3),
rep("pDC",3)),
genes = c("PTPRC", "CD79A", "BANK1", "MS4A1", "CD3E", "CD4", "IL7R", "NKG7",
"KLRD1","CD68", "CD14","ITGAM", "LILRA4", "CLEC4C", "LRRC26"))
p2 <- DO.Dotplot(Seu_object = Seu_obj,
Feature = annotation_Markers,
group.by.x = "seurat_clusters",
plot.margin = c(1,1,1,1),
annotation_x = T,
point_stroke = 0.1,
annotation_x_rev = T,
textSize = 14,
hjust = 0.5,
vjust = 0,
textRot = 0,
segWidth = 0.3,
lwd = 3)
#> Scale for size is already present.
#> Adding another scale for size, which will replace the existing scale.
The manual markers for the immune cells show an agreement for the annotation therefore we can continue with it after some minor adjustments
Seu_obj$annotation <- plyr::revalue(Seu_obj$seurat_clusters, c(`1` = "T_cells",
`2` = "T_cells",
`3` = "NK",
`4` = "B_cells",
`5` = "Monocytes",
`6` = "NK",
`7` = "T_cells",
`8` = "pDC"))
DO.UMAP(Seu_obj, group.by = "annotation", legend.position = "right")
Cell composition
After the identification of the celltype populations, we can also evaluate if there are significant changes in these populations in the healthy and diseased condition using a wrapper function around the python tool scanpro.
DO.CellComposition(Seu_obj,
assay_normalized = "RNA",
cluster_column = "annotation",
sample_column = "orig.ident",
condition_column = "condition",
transform_method = "arcsin",
n_reps = 3)
#> ℹ Using the 'counts' assay as the X matrix
#> [INFO] Your data doesn't have replicates! Artificial replicates will be simulated to run scanpro.
#> [INFO] Simulation may take some minutes...
#> [INFO] Generating 3 replicates and running 100 simulations...
#> [INFO] Finished 100 simulations in 2.17 seconds
#> Using orig.ident, condition as id variables
#> Using condition as id variables
Reclustering of cell populations
Subpopulations can be tricky to find, therefore it is always a good practice to perform a reclustering of a given cell populations, if we are interested in a specific set of cells in a population. Here for example in the T cells. We will identify the subpopulations and then markers defining them.
Seu_obj <- DO.FullRecluster(Seu_obj, over_clustering = "annotation")
DO.UMAP(Seu_obj, group.by = "annotation_recluster")
T_cells <- DO.Subset(Seu_obj,
ident = "annotation_recluster",
ident_name = grep("T_cells", unique(Seu_obj$annotation_recluster), value = T))
#> Specified 'ident_name': expecting a categorical variable.
T_markers <- FindAllMarkers(T_cells, group.by = "annotation_recluster")
#> Calculating cluster T_cells_1
#> Calculating cluster T_cells_2
#> Calculating cluster T_cells_4
#> Calculating cluster T_cells_3
T_cells <- DO.CellTypist(T_cells,
modelName = "Healthy_COVID19_PBMC.pkl",
runCelltypistUpdate=F,
over_clustering = "annotation_recluster",
SeuV5 = F)
#> 2025-06-26 17:49:36 - Running celltypist using model: Healthy_COVID19_PBMC.pkl
#> 2025-06-26 17:49:36 - Saving celltypist results to temporary folder: /tmp/Rtmp9zSWgB/file169324c2391b0
#> 2025-06-26 17:49:45 - Running Celltypist
#> 2025-06-26 17:49:48 - Creating probality plot
T_cells$annotation <- plyr::revalue(T_cells$annotation_recluster, c(`T_cells_1` = "CD4_T_cells",
`T_cells_2` = "CD4_T_cells",
`T_cells_3` = "CD4_T_cells",
`T_cells_4` = "CD8_T_cells"))Now that we identified the marker genes describing the different T cell populations. We can re-annotate them based on their expression profile and a new prediciton from Celltypist. After this we, can easily transfer the labels in the subset to the original object.
Seu_obj <- DO.TransferLabel(Seu_obj,
Subset_obj = T_cells,
annotation_column = "annotation",
subset_annotation = "annotation")
DO.UMAP(Seu_obj, group.by = "annotation", legend.position = "right")
Gene ontology analysis
To explore which biological processes are enriched in a specific cell type across conditions, we can perform gene ontology analysis. We’ll start by identifying differentially expressed genes, focusing here on T cells. For differential gene expression analysis, we introduced a new function, which combines DGE analysis using a single cell approach, e.g. the popular Wilcoxon test and a pseudobulk testing using DESeq2. We can then observe the results in a combined dataframe.
#since this data set contains only one sampel per condition we introduce replicates for showing the pseudo bulk approach
set.seed(123)
Seu_obj$orig.ident2 <- sample(rep(c("A", "B", "C", "D", "E", "F"), length.out = ncol(Seu_obj)))
CD4T_cells <- DO.Subset(Seu_obj, ident = "annotation", ident_name = "CD4_T_cells")
#> Specified 'ident_name': expecting a categorical variable.
DGE_result <- DO.MultiDGE(CD4T_cells,
sample_col = "orig.ident2",
method_sc = "wilcox",
ident_ctrl = "healthy")
#> Names of identity class contain underscores ('_'), replacing with dashes ('-')
#> Centering and scaling data matrix
#>
#> This message is displayed once every 8 hours.
#> 2025-06-26 17:49:50 - Corrected annotation names in pseudo-bulk object by replacing '-' with '_'.
#> 2025-06-26 17:49:50 - Starting DGE single cell method analysis
#> 2025-06-26 17:49:50 - Comparing disease with healthy in: CD4_T_cells
#> 2025-06-26 17:49:50 - Finished DGE single cell method analysis
#> 2025-06-26 17:49:50 - Starting DGE pseudo bulk method analysis
#> 2025-06-26 17:49:50 - Comparing disease with healthy in: CD4_T_cells
#> converting counts to integer mode
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> 2025-06-26 17:49:56 - Finished DGE pseudo bulk method analysis
head(DGE_result,10) %>%
kable(format = "html", table.attr = "style='width:100%;'") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| gene | pct.1 | pct.2 | celltype | condition | avg_log2FC_PB_DESeq2 | avg_log2FC_SC_wilcox | p_val_adj_PB_DESeq2 | p_val_adj_SC_wilcox | p_val_PB_DESeq2 | p_val_SC_wilcox |
|---|---|---|---|---|---|---|---|---|---|---|
| RPS4Y1 | 0.777 | 0.000 | CD4_T_cells | disease | 5.2597526 | 12.9698957 | 0 | 0 | 0 | 0 |
| RGS1 | 0.852 | 0.056 | CD4_T_cells | disease | 5.3593795 | 6.3186953 | 0 | 0 | 0 | 0 |
| CREM | 0.876 | 0.085 | CD4_T_cells | disease | 4.8429628 | 5.5589791 | 0 | 0 | 0 | 0 |
| RPS26 | 0.794 | 1.000 | CD4_T_cells | disease | -3.5365624 | -3.5679504 | 0 | 0 | 0 | 0 |
| SRGN | 0.989 | 0.502 | CD4_T_cells | disease | 4.0234642 | 4.2075868 | 0 | 0 | 0 | 0 |
| TUBB4B | 0.883 | 0.171 | CD4_T_cells | disease | 3.5192993 | 3.9108426 | 0 | 0 | 0 | 0 |
| JUND | 0.993 | 0.959 | CD4_T_cells | disease | 2.0672721 | 2.1379443 | 0 | 0 | 0 | 0 |
| HSPH1 | 0.757 | 0.091 | CD4_T_cells | disease | 4.0478214 | 4.7355979 | 0 | 0 | 0 | 0 |
| CXCR4 | 0.983 | 0.625 | CD4_T_cells | disease | 2.7089540 | 2.7576397 | 0 | 0 | 0 | 0 |
| EEF1A1 | 1.000 | 1.000 | CD4_T_cells | disease | -0.7565409 | -0.7638441 | 0 | 0 | 0 | 0 |
After inspecting the DGE analysis, we continue with
DO.enrichR function, which uses the enrichR API to run gene
set enrichment. It separates the DE genes into up- and down-regulated
sets and runs the analysis for each group independently
result_GO <- DO.enrichR(df_DGE = DGE_result,
gene_column = "gene",
pval_column = "p_val_adj_SC_wilcox",
log2fc_column = "avg_log2FC_SC_wilcox",
pval_cutoff = 0.05,
log2fc_cutoff = 0.25,
path = NULL,
filename = "",
species = "Human",
go_catgs = "GO_Biological_Process_2023")
#> Connection changed to https://maayanlab.cloud/Enrichr/
#> Connection is Live!
#> Uploading data to Enrichr... Done.
#> Querying GO_Biological_Process_2023... Done.
#> Parsing results... Done.
#> Uploading data to Enrichr... Done.
#> Querying GO_Biological_Process_2023... Done.
#> Parsing results... Done.
head(result_GO,5) %>%
kable(format = "html", table.attr = "style='width:100%;'") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Term | Overlap | P.value | Adjusted.P.value | Old.P.value | Old.Adjusted.P.value | Odds.Ratio | Combined.Score | Genes | Database | State |
|---|---|---|---|---|---|---|---|---|---|---|
| Regulation Of DNA-templated Transcription (GO:0006355) | 154/1922 | 0 | 0e+00 | 0 | 0 | 2.069975 | 61.56549 | ZNF331;NAB1;GMNN;ZBTB21;JMJD1C;RORA;PRDM2;ARID4B;PRDM1;AHR;IKZF2;NR3C1;IKZF3;ETS1;BACH1;BASP1;GPBP1;TNFRSF4;JUNB;ZNF165;ZBTB38;F2R;ARID5A;DYRK1B;ARID5B;POU3F1;RUNX3;HIC1;SAP30;JMJD6;MAF;DDIT3;SUB1;MORF4L2;ERF;TOX;ATF3;ATF4;KMT2E;SP140;TSHZ2;CREM;ZFP36L2;ATXN1;MED31;ZNF703;MIER1;HIVEP2;HEXIM1;EGR1;HSPA8;JUN;EGR2;EGR3;JUND;EGR4;CBX4;FUS;DENND4A;PYHIN1;ZBTB10;IRF2BP2;PBX4;FOXN2;SMARCA2;NFKB1;NFKB2;FOSL2;SMAD7;NR4A2;NR4A1;NR4A3;BCL6;ID2;MAFG;ID1;MAFF;JMY;REL;SNAI1;HBP1;CALR;OGT;CDK14;HLA-DRB1;RCOR1;NFE2L2;MXD4;CDK16;CDKN1C;CSRNP1;CSRNP2;BTG1;PHF20;CITED2;HMGB2;CHD2;CHD1;TRPS1;ZNF644;KLF10;NCOA2;BRD2;USP47;RHOH;FOS;BAZ1A;ZFY;SIRT1;MLF1;KLF16;ETV7;TOX2;ELF1;SFPQ;ZEB2;ZEB1;IFNG;IRF4;MED21;IRF1;CEBPA;CEBPB;CEBPG;SPTY2D1;RELB;SERTAD3;IFI16;NFIL3;POLR2A;TP53INP2;TP53INP1;STAT4;RBBP8;PLAGL2;SAP18;POLR2K;POLR2L;HES4;ATF7IP;CD74;STAT3;SOD2;HNRNPAB;KLF3;KLF2;BATF;PER1;KLF6;RYBP;KLF5;PITHD1;PDCD4;FOSB | GO_Biological_Process_2023 | enriched |
| Positive Regulation Of Cytokine Production (GO:0001819) | 44/320 | 0 | 0e+00 | 0 | 0 | 3.575569 | 87.69936 | PTGER4;ITK;RAB1A;CEBPB;CEBPG;RORA;PTPN22;TANK;MALT1;HSPD1;PNP;IFI16;PDE4B;CCR7;TIGIT;HLA-DPA1;IL12RB2;EGR1;CD74;ANXA1;CADM1;RIPK2;PDE4D;STAT3;F2R;ISG15;HLA-A;BATF;SOD1;CD2;SLC7A5;PTPRC;RGCC;IFNG;IRF4;TRAF3;DDIT3;IRF1;CD28;IL6ST;CD200;HSPA1B;ATF4;HSPA1A | GO_Biological_Process_2023 | enriched |
| Regulation Of Gene Expression (GO:0010468) | 98/1127 | 0 | 1e-07 | 0 | 0 | 2.191567 | 50.89466 | ZNF331;TFRC;NAB1;JMJD1C;RORA;PRDM2;AHR;NR3C1;ETS1;BACH1;GPBP1;ZC3H12A;NANOS1;FBXW7;ZBTB38;POU3F1;ATP1B1;RUNX3;HIC1;SAP30;DDIT3;MYADM;EZR;ATF4;CREM;ZBTB1;HNRNPLL;ZFP36L1;PCBP3;ZNF703;MIER1;RBM12;SLC38A2;EGR1;NDFIP2;TIPARP;FUS;DENND4A;RAB27A;BRAF;FOXN2;SMARCA2;NFKB1;NFKB2;CLK1;RGCC;PTPRC;BCL6;ID2;CD28;REL;SNAI1;CALR;OGT;NFE2L2;USP36;SETD2;BTG1;PHF20;CITED2;PTPN22;NFKBIZ;UTY;KDM6B;NCOA2;IFNGR1;RHOH;BAZ1A;SIRT1;MLF1;RNF168;SLC7A5;SFPQ;NPC1;IFNG;TRAF3;CEBPA;CEBPB;AHNAK;SPTY2D1;TOB2;CRIP1;TOB1;SERTAD3;IFI16;NFIL3;POLR2A;TDG;ATF7IP;CD74;STAT3;HNRNPAB;PER1;PDCL3;RYBP;DNAJA4;HSPA1B;HSPA1A | GO_Biological_Process_2023 | enriched |
| Regulation Of Transcription By RNA Polymerase II (GO:0006357) | 150/2028 | 0 | 1e-07 | 0 | 0 | 1.875800 | 42.82043 | ZBTB21;JMJD1C;RORA;PRDM2;ARID4B;PRDM1;AHR;IKZF2;NR3C1;GABPB1;IKZF3;ETS1;BACH1;ZFP36;ZC3H12A;NAMPT;JUNB;IER5;ZNF165;ZBTB38;NSMCE3;ARID5A;ARID5B;POU3F1;RUNX3;HIC1;SAP30;JMJD6;MAF;DDIT3;SUB1;ERF;TOX;EZR;SQSTM1;ATF3;ATF4;SP140;TSHZ2;CREM;ZBTB1;PIK3R1;SDCBP;ATXN1;MED31;MIER1;HIVEP2;HEXIM1;EGR1;PLK3;JUN;EGR2;EGR3;JUND;EGR4;CBX4;FUS;GADD45A;ZBTB10;IRF2BP2;PBX4;SMARCA2;NFKB1;NFKB2;FOSL2;SMAD7;NR4A2;NFKBIA;AHI1;NR4A1;RGCC;NR4A3;BCL6;ID2;MAFG;AGO2;ID1;MAFF;JMY;REL;SNAI1;HBP1;CALR;OGT;RCOR1;NFE2L2;MXD4;ARF4;CSRNP1;CSRNP2;PHF20;CITED2;HMGB2;CHD2;CHD1;TRPS1;ZNF644;TGIF1;KDM6B;KLF10;NCOA2;BRD2;RIPK2;TET2;FOS;BAZ1A;ZFY;SIRT1;YWHAZ;KLF16;ETV7;TOX2;ELF1;SFPQ;ZEB2;ZEB1;IFNG;IRF4;MED21;IRF1;PTMA;CEBPA;MAGEH1;CEBPB;TMF1;CEBPG;DDX21;RELB;DNAJB1;IFI16;NFIL3;DNAJB4;STAT4;RBBP8;PLAGL2;SAP18;MEF2D;HES4;STAT3;SOD2;KLF3;KLF2;BATF;PER1;KLF6;KLF5;FOSB;LPIN1;LPIN2;HSPA1A | GO_Biological_Process_2023 | enriched |
| Response To Unfolded Protein (GO:0006986) | 15/44 | 0 | 2e-07 | 0 | 0 | 11.360859 | 249.29904 | HSPA8;PTPN1;HSP90AA1;HSP90AB1;HSPA4;HSPA2;HSPE1;HERPUD1;HSPD1;DNAJA1;DNAJB1;HSPH1;DNAJB4;DDIT3;HSPA1A | GO_Biological_Process_2023 | enriched |
The top significant results can then be visualized in a bar plot.
result_GO_sig <- result_GO[result_GO$Adjusted.P.value < 0.05, ]
result_GO_sig$celltype <- "CD4T_cells"
DO.SplitBarGSEA(df_GSEA = result_GO_sig,
term_col = "Term",
col_split = "Combined.Score",
cond_col = "State",
pos_cond = "enriched",
showP = F,
path = paste0(base, "/"))
#> !!! Assuming GO Terms are preprocessed (Only Significant terms included)
GSEA_plot <- list.files(path = base, pattern = "SplitBar.*\\.svg$", full.names = TRUE, recursive = TRUE)
plot(magick::image_read_svg(GSEA_plot))
saveRDS(Seu_obj, "~/Downloads/Data10x/Seu_obj_blood10x.rds")Session information
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
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