Experiment 1.3.2

import cv2
import numpy as np
from enum import Enum
import colorsys
import moviepy.editor as mpe
import moviepy
from typing import *
import random
import pandas as pd
import json
import torch
from icecream import ic
import nncolor as nc
import nncolor.data

import IPython
def imshow(img):
    img = img*255
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    _,ret = cv2.imencode('.jpg', img) 
    i = IPython.display.Image(data=ret)
    IPython.display.display(i)

def demo_data():
    FPS = 2
    #frames = [circle_img(p, (255, 255, 255), (50, 20, 20)) for p in range(NUM_POSITIONS)]
    #labels = ['WB-0']*len(frames)
    frames, labels = nc.data.create_samples(30)
    frames = [f*255 for f in frames]
    x_clip = mpe.ImageSequenceClip(frames, fps=2)
    y_clip = mpe.TextClip('WB-0', font='DejaVu-Sans')

    class FrameText(mpe.VideoClip):
        def __init__(self, text, fps):
            def make_frame(f):
               return mpe.TextClip(text[int(f)], font='DejaVu-Sans', color='white').get_frame(f)
            self.duration = 1.0 * len(text) / fps
            mpe.VideoClip.__init__(self, make_frame=make_frame, duration=self.duration)

    y_clip =   FrameText(labels, FPS)
    label_clip = mpe.CompositeVideoClip([mpe.ImageClip(np.zeros(nc.data.DEFAULT_IMG_SHAPE), duration=5), y_clip])
    comp_clip = mpe.clips_array([[y_clip],[x_clip]])
    return comp_clip
clip = demo_data() 
clip.ipython_display(rd_kwargs={'logger':None})

def test_dataset():
    train, test, val = nc.data.load_datasets()
    imshow(val[30]['image'])
test_dataset()

# Copied from: https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

import torchvision as tv
import torchvision.datasets
import torchvision.models
import torchvision.transforms
import torch.nn
import torch.optim
import time
import copy
import os

# Data augmentation and normalization for training
# Just normalization for validation
data_transform = tv.transforms.Compose([
        tv.transforms.ToTensor(),
        tv.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
# Haven't coded up a test loop yet, so ignore test set for now.
colors = nncolor.data.filter_colors(nncolor.data.exp_1_1_data, 
                                    include_colors={'orange', 'brown', 'neither'})
datasets = nncolor.data.train_test_val_split(colors, split_ratio=(11, 0, 7))

train_ds.transform = data_transform
val_ds.transform = data_transform
ds = {'train': train_ds, 'val': val_ds}
dataloaders = {x: torch.utils.data.DataLoader(ds[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(ds[x]) for x in ['train', 'val']}

device = (torch.device("cuda:0") if torch.cuda.is_available() else "cpu")

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            #for inputs, labels in dataloaders[phase]:
            for batch in dataloaders[phase]:
                inputs = batch['image'].to(device)
                labels = batch['label'].to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

# Load & train
model_ft = tv.models.resnet18(pretrained=True)

## [begin] This is where experiment 1.3.2 differs:
for param in model_ft.parameters():
    param.requires_grad = False
## [end]
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = torch.nn.Linear(num_ftrs, 4)
model_ft = model_ft.to(device)
criterion = torch.nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = torch.optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
num_epochs = 20 
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=num_epochs)