docker-python/tests/test_catalyst.py at master · a-mp/docker-python · GitHub

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import unittest

import collections

import json

import numpy as np

import torch

import torch.nn as nn

import torch.nn.functional as F

import torchvision

import torchvision.transforms as transforms

import catalyst

from catalyst.dl import SupervisedRunner, CheckpointCallback

from catalyst import utils

def _to_categorical(y, num_classes=None, dtype='float32'):

"""

Taken from

github.com/keras-team/keras/blob/master/keras/utils/np_utils.py

Converts a class vector (integers) to binary class matrix.

E.g. for use with categorical_crossentropy.

# Arguments

y: class vector to be converted into a matrix

(integers from 0 to num_classes).

num_classes: total number of classes.

dtype: The data type expected by the input, as a string

(`float32`, `float64`, `int32`...)

# Returns

A binary matrix representation of the input. The classes axis

is placed last.

# Example

```python

# Consider an array of 5 labels out of a set of 3 classes {0, 1, 2}:

> labels

array([0, 2, 1, 2, 0])

# `to_categorical` converts this into a matrix with as many

# columns as there are classes. The number of rows

# stays the same.

> to_categorical(labels)

array([[ 1., 0., 0.],

[ 0., 0., 1.],

[ 0., 1., 0.],

[ 0., 0., 1.],

[ 1., 0., 0.]], dtype=float32)

```

"""

y = np.array(y, dtype='int')

input_shape = y.shape

if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:

input_shape = tuple(input_shape[:-1])

y = y.ravel()

if not num_classes:

num_classes = np.max(y) + 1

n = y.shape[0]

categorical = np.zeros((n, num_classes), dtype=dtype)

categorical[np.arange(n), y] = 1

output_shape = input_shape + (num_classes,)

categorical = np.reshape(categorical, output_shape)

return categorical

class Net(nn.Module):

def __init__(self):

super().__init__()

self.conv1 = nn.Conv2d(1, 20, 5, 1)

self.conv2 = nn.Conv2d(20, 50, 5, 1)

self.fc1 = nn.Linear(4 * 4 * 50, 500)

self.fc2 = nn.Linear(500, 10)

def forward(self, x):

x = F.relu(self.conv1(x))

x = F.max_pool2d(x, 2, 2)

x = F.relu(self.conv2(x))

x = F.max_pool2d(x, 2, 2)

x = x.view(-1, 4 * 4 * 50)

x = F.relu(self.fc1(x))

x = self.fc2(x)

return x

class TestCatalyst(unittest.TestCase):

def test_version(self):

self.assertIsNotNone(catalyst.__version__)

def test_mnist(self):

utils.set_global_seed(42)

x_train = np.random.random((100, 1, 28, 28)).astype(np.float32)

y_train = _to_categorical(

np.random.randint(10, size=(100, 1)),

num_classes=10

).astype(np.float32)

x_valid = np.random.random((20, 1, 28, 28)).astype(np.float32)

y_valid = _to_categorical(

np.random.randint(10, size=(20, 1)),

num_classes=10

).astype(np.float32)

x_train, y_train, x_valid, y_valid = \

list(map(torch.tensor, [x_train, y_train, x_valid, y_valid]))

bs = 32

num_workers = 4

data_transform = transforms.ToTensor()

loaders = collections.OrderedDict()

trainset = torch.utils.data.TensorDataset(x_train, y_train)

trainloader = torch.utils.data.DataLoader(

trainset, batch_size=bs,

shuffle=True, num_workers=num_workers)

validset = torch.utils.data.TensorDataset(x_valid, y_valid)

validloader = torch.utils.data.DataLoader(

validset, batch_size=bs,

shuffle=False, num_workers=num_workers)

loaders["train"] = trainloader

loaders["valid"] = validloader

# experiment setup

num_epochs = 3

logdir = "./logs"

# model, criterion, optimizer

model = Net()

criterion = nn.BCEWithLogitsLoss()

optimizer = torch.optim.Adam(model.parameters())

# model runner

runner = SupervisedRunner()

# model training

runner.train(

model=model,

criterion=criterion,

optimizer=optimizer,

loaders=loaders,

logdir=logdir,

num_epochs=num_epochs,

verbose=False,

callbacks=[CheckpointCallback(save_n_best=3)]

)

with open('./logs/checkpoints/_metrics.json') as f:

metrics = json.load(f)

self.assertTrue(metrics['train.3']['loss'] < metrics['train.1']['loss'])

self.assertTrue(metrics['best']['loss'] < 0.35)

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